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SWIRL-Lab
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bigym2-agent-rollouts

Tasks:
Robotics
Languages:
English
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Tags:
robotics
bigym
humanoid
loco-manipulation
coding-agents
evaluation
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Nagi-ovo commited on
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d996b62
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1 Parent(s): c9497a3

Add the final programs of 7 coding agents on all 20 tasks (420 sessions) (part 4)

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  1. policies.csv +421 -0
  2. policies/stack_blocks/deepseek1017-s2/episodes.csv +101 -0
  3. policies/stack_blocks/deepseek1017-s2/policy.py +303 -0
  4. policies/stack_blocks/deepseek1017-s3/episodes.csv +101 -0
  5. policies/stack_blocks/deepseek1017-s3/policy.py +236 -0
  6. policies/stack_blocks/gpt61sol-s1/episodes.csv +101 -0
  7. policies/stack_blocks/gpt61sol-s1/policy.py +207 -0
  8. policies/stack_blocks/gpt61sol-s2/episodes.csv +101 -0
  9. policies/stack_blocks/gpt61sol-s2/policy.py +123 -0
  10. policies/stack_blocks/gpt61sol-s3/episodes.csv +101 -0
  11. policies/stack_blocks/gpt61sol-s3/policy.py +162 -0
  12. policies/stack_blocks/gpt61terra-s1/episodes.csv +101 -0
  13. policies/stack_blocks/gpt61terra-s1/policy.py +76 -0
  14. policies/stack_blocks/gpt61terra-s2/episodes.csv +101 -0
  15. policies/stack_blocks/gpt61terra-s2/policy.py +108 -0
  16. policies/stack_blocks/gpt61terra-s3/episodes.csv +101 -0
  17. policies/stack_blocks/gpt61terra-s3/policy.py +136 -0
  18. policies/stack_blocks/gpt6sol-s1/episodes.csv +101 -0
  19. policies/stack_blocks/gpt6sol-s1/policy.py +198 -0
  20. policies/stack_blocks/gpt6sol-s2/episodes.csv +101 -0
  21. policies/stack_blocks/gpt6sol-s2/policy.py +156 -0
  22. policies/stack_blocks/gpt6sol-s3/episodes.csv +101 -0
  23. policies/stack_blocks/gpt6sol-s3/policy.py +138 -0
  24. policies/stack_blocks/kimi31pro-s1/episodes.csv +101 -0
  25. policies/stack_blocks/kimi31pro-s1/policy.py +379 -0
  26. policies/stack_blocks/kimi31pro-s2/episodes.csv +101 -0
  27. policies/stack_blocks/kimi31pro-s2/policy.py +243 -0
  28. policies/stack_blocks/kimi31pro-s3/episodes.csv +101 -0
  29. policies/stack_blocks/kimi31pro-s3/policy.py +247 -0
  30. policies/stack_blocks/opus-s1/episodes.csv +101 -0
  31. policies/stack_blocks/opus-s1/g1kin.py +97 -0
  32. policies/stack_blocks/opus-s1/headcam.py +55 -0
  33. policies/stack_blocks/opus-s1/percept.py +33 -0
  34. policies/stack_blocks/opus-s1/policy.py +503 -0
  35. policies/stack_blocks/opus-s2/episodes.csv +101 -0
  36. policies/stack_blocks/opus-s2/policy.py +686 -0
  37. policies/stack_blocks/opus-s3/cam.py +51 -0
  38. policies/stack_blocks/opus-s3/episodes.csv +101 -0
  39. policies/stack_blocks/opus-s3/fk.py +132 -0
  40. policies/stack_blocks/opus-s3/policy.py +718 -0
  41. policies/wall_cupboard_close/astra-s1/episodes.csv +101 -0
  42. policies/wall_cupboard_close/astra-s1/policy.py +37 -0
  43. policies/wall_cupboard_close/astra-s2/episodes.csv +101 -0
  44. policies/wall_cupboard_close/astra-s2/policy.py +32 -0
  45. policies/wall_cupboard_close/astra-s3/episodes.csv +101 -0
  46. policies/wall_cupboard_close/astra-s3/policy.py +34 -0
  47. policies/wall_cupboard_close/deepseek1017-s1/episodes.csv +101 -0
  48. policies/wall_cupboard_close/deepseek1017-s1/policy.py +149 -0
  49. policies/wall_cupboard_close/deepseek1017-s2/episodes.csv +101 -0
  50. policies/wall_cupboard_close/deepseek1017-s2/policy.py +122 -0
policies.csv ADDED
@@ -0,0 +1,421 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ task,task_set,session,agent,model,harness,harness_version,effort,service_tier,success_rate,scored_version,policy_lines,budget_used,cost_usd,tokens_input_uncached,tokens_cached,tokens_output,status,files
2
+ dishwasher_close,core,opus-s1,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,0,5,161,97540,20.5678,1133120,32137793,253846,scored,policies/dishwasher_close/opus-s1/policy.py
3
+ dishwasher_close,core,opus-s2,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,0,2,153,101000,25.0822,1365184,43104886,277080,scored,policies/dishwasher_close/opus-s2/policy.py
4
+ dishwasher_close,core,opus-s3,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,100,10,291,93322,15.1431,566146,32371214,207061,scored,policies/dishwasher_close/opus-s3/policy.py
5
+ dishwasher_load_cups,core,opus-s1,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,83,78,745,100443,35.1785,1026158,96381670,384765,scored,policies/dishwasher_load_cups/opus-s1/policy.py
6
+ dishwasher_load_cups,core,opus-s2,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,1,47,440,93009,20.9347,709428,51484778,245087,scored,policies/dishwasher_load_cups/opus-s2/policy.py
7
+ dishwasher_load_cups,core,opus-s3,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,1,92,897,93571,27.4713,1019723,64438875,321410,scored,policies/dishwasher_load_cups/opus-s3/policy.py;policies/dishwasher_load_cups/opus-s3/g1kin.py;policies/dishwasher_load_cups/opus-s3/vision.py
8
+ drawer_top_close,core,opus-s1,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,100,5,76,76438,1.1959,56304,1645669,20834,scored,policies/drawer_top_close/opus-s1/policy.py
9
+ drawer_top_close,core,opus-s2,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,100,2,102,62872,3.0479,118863,5070562,54170,scored,policies/drawer_top_close/opus-s2/policy.py
10
+ drawer_top_close,core,opus-s3,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,100,2,83,39811,0.7328,41639,683259,13158,scored,policies/drawer_top_close/opus-s3/policy.py
11
+ drawer_top_open,core,opus-s1,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,99,40,280,96255,11.3522,313708,27804385,164149,scored,policies/drawer_top_open/opus-s1/policy.py
12
+ drawer_top_open,core,opus-s2,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,98,23,410,72905,7.3521,251482,15127712,115777,scored,policies/drawer_top_open/opus-s2/policy.py;policies/drawer_top_open/opus-s2/g1kin.py;policies/drawer_top_open/opus-s2/handles.py
13
+ drawer_top_open,core,opus-s3,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,100,67,491,96799,14.276,563764,33987631,148490,scored,policies/drawer_top_open/opus-s3/policy.py;policies/drawer_top_open/opus-s3/headcam.py;policies/drawer_top_open/opus-s3/kin.py
14
+ move_plate,core,opus-s1,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,21,40,648,99896,24.5393,797274,58034979,327799,scored,policies/move_plate/opus-s1/policy.py
15
+ move_plate,core,opus-s2,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,15,20,532,94087,14.3046,369119,35091038,216740,scored,policies/move_plate/opus-s2/policy.py
16
+ move_plate,core,opus-s3,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,23,53,692,95511,44.5888,650252,58262275,273955,scored,policies/move_plate/opus-s3/policy.py;policies/move_plate/opus-s3/ctrl.py;policies/move_plate/opus-s3/fk.py;policies/move_plate/opus-s3/vision.py
17
+ move_two_plates,core,opus-s1,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,8,104,807,98595,21.2487,515493,55727710,299046,scored,policies/move_two_plates/opus-s1/policy.py
18
+ move_two_plates,core,opus-s2,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,15,55,475,99431,25.7318,1021489,52229355,355796,scored,policies/move_two_plates/opus-s2/policy.py
19
+ move_two_plates,core,opus-s3,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,22,71,509,96262,22.1373,471033,63574291,282810,scored,policies/move_two_plates/opus-s3/policy.py
20
+ pick_box,core,opus-s1,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,44,21,435,98066,11.0736,314827,26845900,159349,scored,policies/pick_box/opus-s1/policy.py
21
+ pick_box,core,opus-s2,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,99,19,352,95241,8.6046,294983,18720577,125075,scored,policies/pick_box/opus-s2/policy.py
22
+ pick_box,core,opus-s3,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,71,55,458,87969,12.1171,328279,30767594,162469,scored,policies/pick_box/opus-s3/policy.py
23
+ reach_target_dual,core,opus-s1,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,71,25,600,92723,9.509,284849,19598954,165575,scored,policies/reach_target_dual/opus-s1/policy.py
24
+ reach_target_dual,core,opus-s2,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,99,30,619,78293,8.2644,257355,17205019,138278,scored,policies/reach_target_dual/opus-s2/policy.py;policies/reach_target_dual/opus-s2/g1reach_geom.py;policies/reach_target_dual/opus-s2/g1reach_vision.py
25
+ reach_target_dual,core,opus-s3,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,65,22,586,97748,12.7152,326052,33161318,173800,scored,policies/reach_target_dual/opus-s3/policy.py
26
+ reach_target_multi_modal,core,opus-s1,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,100,25,514,72915,6.2168,217025,11210367,111963,scored,policies/reach_target_multi_modal/opus-s1/policy.py
27
+ reach_target_multi_modal,core,opus-s2,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,100,12,431,63229,11.4473,203072,13483866,96862,scored,policies/reach_target_multi_modal/opus-s2/policy.py
28
+ reach_target_multi_modal,core,opus-s3,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,100,13,416,76234,5.4287,188612,9819067,97836,scored,policies/reach_target_multi_modal/opus-s3/policy.py
29
+ dishwasher_load_cutlery,extended,opus-s1,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,12,23,414,98591,21.7517,632187,55739064,277429,scored,policies/dishwasher_load_cutlery/opus-s1/policy.py
30
+ dishwasher_load_cutlery,extended,opus-s2,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,12,76,653,97562,26.3891,773789,72029314,289764,scored,policies/dishwasher_load_cutlery/opus-s2/policy.py;policies/dishwasher_load_cutlery/opus-s2/g1kin.py
31
+ dishwasher_load_cutlery,extended,opus-s3,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,5,44,719,98242,30.8292,1401407,61153240,369463,scored,policies/dishwasher_load_cutlery/opus-s3/policy.py;policies/dishwasher_load_cutlery/opus-s3/kin.py;policies/dishwasher_load_cutlery/opus-s3/vision.py
32
+ dishwasher_load_plates,extended,opus-s1,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,38,5,504,98993,28.738,802629,78612684,329847,scored,policies/dishwasher_load_plates/opus-s1/policy.py;policies/dishwasher_load_plates/opus-s1/g1lib.py
33
+ dishwasher_load_plates,extended,opus-s2,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,0,108,844,69573,23.0032,717072,53233481,331096,crashed,policies/dishwasher_load_plates/opus-s2/policy.py;policies/dishwasher_load_plates/opus-s2/camgeo.py;policies/dishwasher_load_plates/opus-s2/g1fk.py
34
+ dishwasher_load_plates,extended,opus-s3,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,1,128,778,98474,31.7166,912451,85541156,365562,scored,policies/dishwasher_load_plates/opus-s3/policy.py;policies/dishwasher_load_plates/opus-s3/camera.py;policies/dishwasher_load_plates/opus-s3/g1kin.py;policies/dishwasher_load_plates/opus-s3/vision.py
35
+ flip_cup,extended,opus-s1,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,91,22,457,96178,14.7238,395543,32962244,248415,scored,policies/flip_cup/opus-s1/policy.py;policies/flip_cup/opus-s1/g1kin.py
36
+ flip_cup,extended,opus-s2,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,97,24,379,99151,8.8696,288161,17966606,148595,scored,policies/flip_cup/opus-s2/policy.py
37
+ flip_cup,extended,opus-s3,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,94,15,348,93526,15.6327,420664,38350075,224531,scored,policies/flip_cup/opus-s3/policy.py
38
+ flip_cutlery,extended,opus-s1,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,0,18,892,93601,47.1235,750166,55369141,326445,crashed,policies/flip_cutlery/opus-s1/policy.py;policies/flip_cutlery/opus-s1/g1lib.py
39
+ flip_cutlery,extended,opus-s2,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,70,22,514,98544,29.422,774697,78684158,374504,scored,policies/flip_cutlery/opus-s2/policy.py
40
+ flip_cutlery,extended,opus-s3,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,19,65,965,99477,25.5416,824521,67359092,273780,scored,policies/flip_cutlery/opus-s3/policy.py;policies/flip_cutlery/opus-s3/g1kin.py;policies/flip_cutlery/opus-s3/vision.py
41
+ put_cups,extended,opus-s1,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,14,24,685,100773,23.5961,471030,72129014,270212,scored,policies/put_cups/opus-s1/policy.py
42
+ put_cups,extended,opus-s2,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,0,75,1034,90637,34.0351,1270191,85736420,336430,scored,policies/put_cups/opus-s2/policy.py;policies/put_cups/opus-s2/cupvision.py;policies/put_cups/opus-s2/g1kin.py
43
+ put_cups,extended,opus-s3,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,0,114,1054,94174,28.2013,907163,74850528,298800,scored,policies/put_cups/opus-s3/policy.py;policies/put_cups/opus-s3/g1kin.py;policies/put_cups/opus-s3/vision.py
44
+ reach_target_single,extended,opus-s1,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,100,21,313,75936,5.5224,188338,9945105,101370,scored,policies/reach_target_single/opus-s1/policy.py
45
+ reach_target_single,extended,opus-s2,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,99,6,282,63432,2.6867,110452,3708444,53089,scored,policies/reach_target_single/opus-s2/policy.py
46
+ reach_target_single,extended,opus-s3,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,100,5,304,61465,3.3768,143837,4269392,68634,scored,policies/reach_target_single/opus-s3/policy.py
47
+ sandwich_remove,extended,opus-s1,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,0,41,1452,83741,51.8504,986747,52274058,380705,scored,policies/sandwich_remove/opus-s1/policy.py
48
+ sandwich_remove,extended,opus-s2,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,0,3,577,94323,35.3219,1219015,86497651,413639,scored,policies/sandwich_remove/opus-s2/policy.py;policies/sandwich_remove/opus-s2/g1kin.py
49
+ sandwich_remove,extended,opus-s3,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,1,83,906,86442,19.3325,633358,48405165,229324,scored,policies/sandwich_remove/opus-s3/policy.py;policies/sandwich_remove/opus-s3/camera.py;policies/sandwich_remove/opus-s3/g1kin.py;policies/sandwich_remove/opus-s3/vision.py
50
+ saucepan_to_hob,extended,opus-s1,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,0,2,1181,96315,44.7686,501879,60833144,313099,scored,policies/saucepan_to_hob/opus-s1/policy.py;policies/saucepan_to_hob/opus-s1/g1fk.py;policies/saucepan_to_hob/opus-s1/g1ik.py;policies/saucepan_to_hob/opus-s1/headcam.py;policies/saucepan_to_hob/opus-s1/planner.py;policies/saucepan_to_hob/opus-s1/skills.py;policies/saucepan_to_hob/opus-s1/skills2.py
51
+ saucepan_to_hob,extended,opus-s2,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,25,117,841,96902,33.8742,601773,110019845,352958,scored,policies/saucepan_to_hob/opus-s2/policy.py;policies/saucepan_to_hob/opus-s2/cam.py;policies/saucepan_to_hob/opus-s2/kin.py
52
+ saucepan_to_hob,extended,opus-s3,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,0,27,670,100619,26.2397,670116,73566161,308422,scored,policies/saucepan_to_hob/opus-s3/policy.py;policies/saucepan_to_hob/opus-s3/camera.py;policies/saucepan_to_hob/opus-s3/g1fk.py;policies/saucepan_to_hob/opus-s3/vision.py
53
+ stack_blocks,extended,opus-s1,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,100,60,691,100186,13.3625,311348,35380984,189859,scored,policies/stack_blocks/opus-s1/policy.py;policies/stack_blocks/opus-s1/g1kin.py;policies/stack_blocks/opus-s1/headcam.py;policies/stack_blocks/opus-s1/percept.py
54
+ stack_blocks,extended,opus-s2,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,0,51,686,99747,20.4689,463202,58920920,249056,scored,policies/stack_blocks/opus-s2/policy.py
55
+ stack_blocks,extended,opus-s3,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,1,55,903,98547,18.4384,438427,50722627,239411,scored,policies/stack_blocks/opus-s3/policy.py;policies/stack_blocks/opus-s3/cam.py;policies/stack_blocks/opus-s3/fk.py
56
+ wall_cupboard_close,extended,opus-s1,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,100,8,227,54297,3.8848,145748,7020095,65771,scored,policies/wall_cupboard_close/opus-s1/policy.py
57
+ wall_cupboard_close,extended,opus-s2,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,100,8,215,71831,5.7503,192422,10439327,106194,scored,policies/wall_cupboard_close/opus-s2/policy.py
58
+ wall_cupboard_close,extended,opus-s3,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,100,14,308,83224,12.167,360281,29352788,170771,scored,policies/wall_cupboard_close/opus-s3/policy.py
59
+ wall_cupboard_open,extended,opus-s1,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,100,44,516,58867,16.2642,411076,47936895,169501,scored,policies/wall_cupboard_open/opus-s1/policy.py
60
+ wall_cupboard_open,extended,opus-s2,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,100,67,584,66416,14.4947,382475,36751337,204308,scored,policies/wall_cupboard_open/opus-s2/policy.py;policies/wall_cupboard_open/opus-s2/ctrl.py;policies/wall_cupboard_open/opus-s2/fk.py;policies/wall_cupboard_open/opus-s2/vision.py
61
+ wall_cupboard_open,extended,opus-s3,opus55,Claude Opus 5.5,Claude Code,2.1.280 (Claude Code),high,default,100,42,503,86142,21.2168,488020,60100953,264711,scored,policies/wall_cupboard_open/opus-s3/policy.py
62
+ dishwasher_close,core,astra-s1,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,100,2,58,93180,8.29,122103,6026112,20770,scored,policies/dishwasher_close/astra-s1/policy.py
63
+ dishwasher_close,core,astra-s2,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,100,3,44,77778,20.03,165712,16604288,35430,scored,policies/dishwasher_close/astra-s2/policy.py
64
+ dishwasher_close,core,astra-s3,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,100,2,67,94948,6.91,155149,4026240,26569,scored,policies/dishwasher_close/astra-s3/policy.py
65
+ dishwasher_load_cups,core,astra-s1,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,18,16,541,100489,31.51,386004,24206464,68817,scored,policies/dishwasher_load_cups/astra-s1/policy.py
66
+ dishwasher_load_cups,core,astra-s2,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,16,26,163,101000,23.24,238919,17284736,71387,scored,policies/dishwasher_load_cups/astra-s2/policy.py
67
+ dishwasher_load_cups,core,astra-s3,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,30,11,140,100490,18.38,212694,13128832,62401,scored,policies/dishwasher_load_cups/astra-s3/policy.py
68
+ drawer_top_close,core,astra-s1,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,96,3,19,44059,1.6,62045,792320,3678,scored,policies/drawer_top_close/astra-s1/policy.py
69
+ drawer_top_close,core,astra-s2,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,100,4,35,38773,1.34,33861,768384,4667,scored,policies/drawer_top_close/astra-s2/policy.py
70
+ drawer_top_close,core,astra-s3,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,100,2,33,39653,1.93,42585,1217536,5757,scored,policies/drawer_top_close/astra-s3/policy.py
71
+ drawer_top_open,core,astra-s1,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,100,13,77,99558,7.51,101607,5139712,27052,scored,policies/drawer_top_open/astra-s1/policy.py
72
+ drawer_top_open,core,astra-s2,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,97,4,32,100624,5.24,82794,3558144,17174,scored,policies/drawer_top_open/astra-s2/policy.py
73
+ drawer_top_open,core,astra-s3,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,100,3,34,81799,6.94,136351,4522368,21177,scored,policies/drawer_top_open/astra-s3/policy.py
74
+ move_plate,core,astra-s1,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,36,8,213,99651,35.27,593640,24072192,105312,scored,policies/move_plate/astra-s1/policy.py
75
+ move_plate,core,astra-s2,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,2,7,81,100806,10.91,175551,7486976,33294,scored,policies/move_plate/astra-s2/policy.py
76
+ move_plate,core,astra-s3,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,4,17,135,100950,10.14,165785,6296192,43749,scored,policies/move_plate/astra-s3/policy.py
77
+ move_two_plates,core,astra-s1,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,0,10,126,101000,10.96,176690,7036288,43192,scored,policies/move_two_plates/astra-s1/policy.py
78
+ move_two_plates,core,astra-s2,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,38,11,81,101000,9.77,207720,6020096,33412,scored,policies/move_two_plates/astra-s2/policy.py
79
+ move_two_plates,core,astra-s3,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,9,18,128,99449,16.26,189610,11486720,57476,scored,policies/move_two_plates/astra-s3/policy.py
80
+ pick_box,core,astra-s1,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,64,10,86,99951,11.94,215735,8406528,27464,scored,policies/pick_box/astra-s1/policy.py
81
+ pick_box,core,astra-s2,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,85,9,115,100912,13.39,302846,8494592,37243,scored,policies/pick_box/astra-s2/policy.py
82
+ pick_box,core,astra-s3,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,12,5,98,100989,12.31,152527,8983808,36106,scored,policies/pick_box/astra-s3/policy.py
83
+ reach_target_dual,core,astra-s1,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,100,16,168,100688,5.61,88871,3438592,25740,scored,policies/reach_target_dual/astra-s1/policy.py
84
+ reach_target_dual,core,astra-s2,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,93,13,96,100787,6.95,197016,3795840,23626,scored,policies/reach_target_dual/astra-s2/policy.py
85
+ reach_target_dual,core,astra-s3,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,75,16,118,100505,5.38,131429,2925056,22763,scored,policies/reach_target_dual/astra-s3/policy.py
86
+ reach_target_multi_modal,core,astra-s1,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,75,11,86,100708,5.1,69649,3254528,22965,scored,policies/reach_target_multi_modal/astra-s1/policy.py
87
+ reach_target_multi_modal,core,astra-s2,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,97,7,71,91340,3.27,66544,1918336,13633,scored,policies/reach_target_multi_modal/astra-s2/policy.py
88
+ reach_target_multi_modal,core,astra-s3,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,95,14,120,100790,5.55,145474,2887296,24153,scored,policies/reach_target_multi_modal/astra-s3/policy.py
89
+ dishwasher_load_cutlery,extended,astra-s1,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,5,8,187,100425,32.04,537960,23006208,72998,scored,policies/dishwasher_load_cutlery/astra-s1/policy.py
90
+ dishwasher_load_cutlery,extended,astra-s2,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,0,19,134,100596,31.93,430713,23540992,81705,scored,policies/dishwasher_load_cutlery/astra-s2/policy.py
91
+ dishwasher_load_cutlery,extended,astra-s3,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,0,26,92,100292,38.35,568538,28855808,76077,scored,policies/dishwasher_load_cutlery/astra-s3/policy.py
92
+ dishwasher_load_plates,extended,astra-s1,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,2,9,76,99944,15.76,231241,10711808,54755,scored,policies/dishwasher_load_plates/astra-s1/policy.py
93
+ dishwasher_load_plates,extended,astra-s2,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,3,25,141,100093,13.43,312520,7859072,49009,scored,policies/dishwasher_load_plates/astra-s2/policy.py
94
+ dishwasher_load_plates,extended,astra-s3,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,6,18,72,99930,18.92,234380,13420928,63154,scored,policies/dishwasher_load_plates/astra-s3/policy.py
95
+ flip_cup,extended,astra-s1,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,29,14,143,100011,6.02,103289,3514624,29431,scored,policies/flip_cup/astra-s1/policy.py
96
+ flip_cup,extended,astra-s2,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,59,17,226,101000,12.81,284696,7709696,45109,scored,policies/flip_cup/astra-s2/policy.py
97
+ flip_cup,extended,astra-s3,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,27,8,176,100973,10.42,149383,6733696,43808,scored,policies/flip_cup/astra-s3/policy.py
98
+ flip_cutlery,extended,astra-s1,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,3,2,120,100990,18.06,180636,13646336,52123,scored,policies/flip_cutlery/astra-s1/policy.py
99
+ flip_cutlery,extended,astra-s2,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,0,7,140,100766,20.99,259702,15024128,67307,scored,policies/flip_cutlery/astra-s2/policy.py
100
+ flip_cutlery,extended,astra-s3,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,0,4,84,100995,18.69,291977,12728704,60881,scored,policies/flip_cutlery/astra-s3/policy.py
101
+ put_cups,extended,astra-s1,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,0,9,139,100990,31.84,455674,23117824,83221,scored,policies/put_cups/astra-s1/policy.py
102
+ put_cups,extended,astra-s2,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,15,7,252,100411,35.41,567932,25012736,94378,scored,policies/put_cups/astra-s2/policy.py
103
+ put_cups,extended,astra-s3,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,9,12,185,100779,33.52,419729,25229312,81862,scored,policies/put_cups/astra-s3/policy.py
104
+ reach_target_single,extended,astra-s1,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,98,9,93,84355,2.3,50662,1257472,10762,scored,policies/reach_target_single/astra-s1/policy.py
105
+ reach_target_single,extended,astra-s2,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,97,2,49,51150,1.95,54615,906880,9890,scored,policies/reach_target_single/astra-s2/policy.py
106
+ reach_target_single,extended,astra-s3,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,100,7,133,87460,3.57,92040,1680640,19328,scored,policies/reach_target_single/astra-s3/policy.py
107
+ sandwich_remove,extended,astra-s1,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,15,23,75,100996,18.82,259718,13591808,52539,scored,policies/sandwich_remove/astra-s1/policy.py
108
+ sandwich_remove,extended,astra-s2,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,0,19,109,100987,20.43,292448,15093504,48256,scored,policies/sandwich_remove/astra-s2/policy.py
109
+ sandwich_remove,extended,astra-s3,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,0,2,107,100707,23.16,324564,16173568,74811,scored,policies/sandwich_remove/astra-s3/policy.py
110
+ saucepan_to_hob,extended,astra-s1,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,14,13,102,100596,40.51,490089,32914048,53973,scored,policies/saucepan_to_hob/astra-s1/policy.py
111
+ saucepan_to_hob,extended,astra-s2,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,18,10,83,99657,19.9,214314,15634048,42357,scored,policies/saucepan_to_hob/astra-s2/policy.py
112
+ saucepan_to_hob,extended,astra-s3,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,47,12,108,99303,31.22,276486,25323136,62661,scored,policies/saucepan_to_hob/astra-s3/policy.py
113
+ stack_blocks,extended,astra-s1,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,1,21,190,100921,15.03,214461,9778048,62184,scored,policies/stack_blocks/astra-s1/policy.py
114
+ stack_blocks,extended,astra-s2,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,2,11,182,100980,9.12,136704,5114624,52802,scored,policies/stack_blocks/astra-s2/policy.py
115
+ stack_blocks,extended,astra-s3,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,0,12,182,100487,15.61,180803,11342592,49270,scored,policies/stack_blocks/astra-s3/policy.py
116
+ wall_cupboard_close,extended,astra-s1,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,100,2,37,58670,2.24,56232,1209216,9328,scored,policies/wall_cupboard_close/astra-s1/policy.py
117
+ wall_cupboard_close,extended,astra-s2,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,100,1,32,77232,2.89,61133,1617536,13291,scored,policies/wall_cupboard_close/astra-s2/policy.py
118
+ wall_cupboard_close,extended,astra-s3,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,default,100,1,34,94540,1.87,43019,1073280,7423,scored,policies/wall_cupboard_close/astra-s3/policy.py
119
+ wall_cupboard_open,extended,astra-s1,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,0,2,74,99746,11.56,245305,6689792,48351,scored,policies/wall_cupboard_open/astra-s1/policy.py
120
+ wall_cupboard_open,extended,astra-s2,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,100,2,58,95955,5.53,89536,3909248,14555,scored,policies/wall_cupboard_open/astra-s2/policy.py
121
+ wall_cupboard_open,extended,astra-s3,astra,GPT-6 Astra,Codex CLI,codex-cli 0.153.4,high,fast,100,1,97,82617,10.75,148044,6993664,45502,scored,policies/wall_cupboard_open/astra-s3/policy.py
122
+ dishwasher_close,core,gpt6sol-s1,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,0,4,99,100190,4.94,222736,19202176,65210,scored,policies/dishwasher_close/gpt6sol-s1/policy.py
123
+ dishwasher_close,core,gpt6sol-s2,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,0,4,139,97513,8.34,443554,32466304,96339,scored,policies/dishwasher_close/gpt6sol-s2/policy.py
124
+ dishwasher_close,core,gpt6sol-s3,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,0,10,100,100465,7.47,1036477,21887616,101699,scored,policies/dishwasher_close/gpt6sol-s3/policy.py
125
+ dishwasher_load_cups,core,gpt6sol-s1,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,2,4,83,100298,8.52,450665,34261120,76388,scored,policies/dishwasher_load_cups/gpt6sol-s1/policy.py
126
+ dishwasher_load_cups,core,gpt6sol-s2,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,0,1,108,99186,11.9,467302,49467776,107496,scored,policies/dishwasher_load_cups/gpt6sol-s2/policy.py
127
+ dishwasher_load_cups,core,gpt6sol-s3,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,3,18,178,100738,9.84,488716,39528448,95426,scored,policies/dishwasher_load_cups/gpt6sol-s3/policy.py
128
+ drawer_top_close,core,gpt6sol-s1,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,98,4,19,98624,0.96,70330,3215232,18033,scored,policies/drawer_top_close/gpt6sol-s1/policy.py
129
+ drawer_top_close,core,gpt6sol-s2,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,100,1,22,62982,0.87,84544,2557952,18790,scored,policies/drawer_top_close/gpt6sol-s2/policy.py
130
+ drawer_top_close,core,gpt6sol-s3,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,100,2,33,99493,1.89,157670,6451456,28761,scored,policies/drawer_top_close/gpt6sol-s3/policy.py
131
+ drawer_top_open,core,gpt6sol-s1,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,55,6,99,100515,4.48,212238,17338368,59253,scored,policies/drawer_top_open/gpt6sol-s1/policy.py
132
+ drawer_top_open,core,gpt6sol-s2,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,66,4,46,100451,5.62,355132,21216256,66358,scored,policies/drawer_top_open/gpt6sol-s2/policy.py
133
+ drawer_top_open,core,gpt6sol-s3,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,36,7,55,100467,3.87,192760,15085184,46667,scored,policies/drawer_top_open/gpt6sol-s3/policy.py
134
+ move_plate,core,gpt6sol-s1,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,1,2,78,100940,4.93,428716,17004800,67518,scored,policies/move_plate/gpt6sol-s1/policy.py
135
+ move_plate,core,gpt6sol-s2,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,7,1,46,98450,4.49,153898,18555520,46954,scored,policies/move_plate/gpt6sol-s2/policy.py
136
+ move_plate,core,gpt6sol-s3,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,5,4,95,100985,3.16,276824,10279936,54879,scored,policies/move_plate/gpt6sol-s3/policy.py
137
+ move_two_plates,core,gpt6sol-s1,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,0,5,125,99976,6.98,250929,28520960,77161,scored,policies/move_two_plates/gpt6sol-s1/policy.py
138
+ move_two_plates,core,gpt6sol-s2,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,0,2,111,100919,7.47,272352,31418624,63844,scored,policies/move_two_plates/gpt6sol-s2/policy.py
139
+ move_two_plates,core,gpt6sol-s3,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,0,2,41,100719,4.23,300144,15474944,53221,scored,policies/move_two_plates/gpt6sol-s3/policy.py
140
+ pick_box,core,gpt6sol-s1,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,0,2,147,98440,8.73,929378,29795200,91495,scored,policies/pick_box/gpt6sol-s1/policy.py
141
+ pick_box,core,gpt6sol-s2,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,0,3,81,100242,6.29,432872,23279360,76901,scored,policies/pick_box/gpt6sol-s2/policy.py
142
+ pick_box,core,gpt6sol-s3,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,0,2,152,100815,2.67,125885,9573888,50417,scored,policies/pick_box/gpt6sol-s3/policy.py
143
+ reach_target_dual,core,gpt6sol-s1,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,33,5,125,100190,4.32,190172,16452480,64887,scored,policies/reach_target_dual/gpt6sol-s1/policy.py
144
+ reach_target_dual,core,gpt6sol-s2,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,58,8,119,101000,3.41,182509,12503936,54014,scored,policies/reach_target_dual/gpt6sol-s2/policy.py
145
+ reach_target_dual,core,gpt6sol-s3,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,20,9,97,100329,3.66,212669,12790144,67186,scored,policies/reach_target_dual/gpt6sol-s3/policy.py
146
+ reach_target_multi_modal,core,gpt6sol-s1,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,73,6,73,100221,1.16,84385,3576320,27682,scored,policies/reach_target_multi_modal/gpt6sol-s1/policy.py
147
+ reach_target_multi_modal,core,gpt6sol-s2,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,86,7,88,100444,1.32,83615,4431872,26896,scored,policies/reach_target_multi_modal/gpt6sol-s2/policy.py
148
+ reach_target_multi_modal,core,gpt6sol-s3,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,default,99,11,98,100197,1.22,88363,3609344,31930,scored,policies/reach_target_multi_modal/gpt6sol-s3/policy.py
149
+ dishwasher_load_cutlery,extended,gpt6sol-s1,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,1,4,142,100387,4.0,377518,13077632,63228,scored,policies/dishwasher_load_cutlery/gpt6sol-s1/policy.py
150
+ dishwasher_load_cutlery,extended,gpt6sol-s2,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,2,107,100882,4.05,248219,15073152,53917,scored,policies/dishwasher_load_cutlery/gpt6sol-s2/policy.py
151
+ dishwasher_load_cutlery,extended,gpt6sol-s3,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,2,97,100909,4.39,384367,14751488,66674,scored,policies/dishwasher_load_cutlery/gpt6sol-s3/policy.py
152
+ dishwasher_load_plates,extended,gpt6sol-s1,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,3,115,100717,4.82,227375,18108288,74773,scored,policies/dishwasher_load_plates/gpt6sol-s1/policy.py
153
+ dishwasher_load_plates,extended,gpt6sol-s2,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,4,63,100485,3.42,151079,12883840,54030,scored,policies/dishwasher_load_plates/gpt6sol-s2/policy.py
154
+ dishwasher_load_plates,extended,gpt6sol-s3,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,6,116,100705,3.69,208885,12863104,69497,scored,policies/dishwasher_load_plates/gpt6sol-s3/policy.py
155
+ flip_cup,extended,gpt6sol-s1,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,5,131,100344,7.09,219927,29419136,76484,scored,policies/flip_cup/gpt6sol-s1/policy.py
156
+ flip_cup,extended,gpt6sol-s2,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,1,3,109,100805,6.8,436842,26130688,70005,scored,policies/flip_cup/gpt6sol-s2/policy.py
157
+ flip_cup,extended,gpt6sol-s3,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,1,3,34,100798,2.0,178660,6027392,44013,scored,policies/flip_cup/gpt6sol-s3/policy.py
158
+ flip_cutlery,extended,gpt6sol-s1,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,2,119,100806,4.76,411860,15615616,81550,scored,policies/flip_cutlery/gpt6sol-s1/policy.py
159
+ flip_cutlery,extended,gpt6sol-s2,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,2,2,109,100527,4.49,185591,17486592,61752,scored,policies/flip_cutlery/gpt6sol-s2/policy.py
160
+ flip_cutlery,extended,gpt6sol-s3,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,1,3,108,99050,2.53,161526,8312192,54394,scored,policies/flip_cutlery/gpt6sol-s3/policy.py
161
+ put_cups,extended,gpt6sol-s1,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,6,97,100128,7.07,458834,26926464,76592,scored,policies/put_cups/gpt6sol-s1/policy.py
162
+ put_cups,extended,gpt6sol-s2,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,1,159,100557,6.21,256511,23709184,95621,scored,policies/put_cups/gpt6sol-s2/policy.py
163
+ put_cups,extended,gpt6sol-s3,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,3,154,100985,7.04,454176,25980544,93369,scored,policies/put_cups/gpt6sol-s3/policy.py
164
+ reach_target_single,extended,gpt6sol-s1,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,80,4,70,100272,1.74,121422,5645056,36639,scored,policies/reach_target_single/gpt6sol-s1/policy.py
165
+ reach_target_single,extended,gpt6sol-s2,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,53,3,98,100493,1.5,101846,4487552,39586,scored,policies/reach_target_single/gpt6sol-s2/policy.py
166
+ reach_target_single,extended,gpt6sol-s3,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,50,3,83,100972,0.76,64388,1802624,27186,scored,policies/reach_target_single/gpt6sol-s3/policy.py
167
+ sandwich_remove,extended,gpt6sol-s1,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,1,81,100644,3.41,199414,12395008,52881,scored,policies/sandwich_remove/gpt6sol-s1/policy.py
168
+ sandwich_remove,extended,gpt6sol-s2,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,3,89,100662,10.56,514886,41831680,116536,scored,policies/sandwich_remove/gpt6sol-s2/policy.py
169
+ sandwich_remove,extended,gpt6sol-s3,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,3,81,100511,6.89,524306,24896640,86701,scored,policies/sandwich_remove/gpt6sol-s3/policy.py
170
+ saucepan_to_hob,extended,gpt6sol-s1,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,4,133,100955,7.94,251222,33142528,81107,scored,policies/saucepan_to_hob/gpt6sol-s1/policy.py
171
+ saucepan_to_hob,extended,gpt6sol-s2,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,1,125,100868,2.81,187305,9603968,51104,scored,policies/saucepan_to_hob/gpt6sol-s2/policy.py
172
+ saucepan_to_hob,extended,gpt6sol-s3,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,1,87,100990,3.37,161874,12737664,49443,scored,policies/saucepan_to_hob/gpt6sol-s3/policy.py
173
+ stack_blocks,extended,gpt6sol-s1,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,8,198,100696,8.83,402316,35649408,89532,scored,policies/stack_blocks/gpt6sol-s1/policy.py
174
+ stack_blocks,extended,gpt6sol-s2,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,4,156,99720,4.33,168357,17058048,57992,scored,policies/stack_blocks/gpt6sol-s2/policy.py
175
+ stack_blocks,extended,gpt6sol-s3,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,2,138,100906,2.02,122265,6534784,47156,scored,policies/stack_blocks/gpt6sol-s3/policy.py
176
+ wall_cupboard_close,extended,gpt6sol-s1,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,100,1,43,100581,1.53,130902,4572160,35170,scored,policies/wall_cupboard_close/gpt6sol-s1/policy.py
177
+ wall_cupboard_close,extended,gpt6sol-s2,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,100,1,32,54886,0.93,125723,2324480,21411,scored,policies/wall_cupboard_close/gpt6sol-s2/policy.py
178
+ wall_cupboard_close,extended,gpt6sol-s3,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,2,95,100325,1.93,131797,6223360,41939,scored,policies/wall_cupboard_close/gpt6sol-s3/policy.py
179
+ wall_cupboard_open,extended,gpt6sol-s1,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,2,72,100870,7.85,799166,27047424,84150,scored,policies/wall_cupboard_open/gpt6sol-s1/policy.py
180
+ wall_cupboard_open,extended,gpt6sol-s2,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,3,36,101000,3.41,192374,11239040,77915,scored,policies/wall_cupboard_open/gpt6sol-s2/policy.py
181
+ wall_cupboard_open,extended,gpt6sol-s3,gpt6sol,GPT-6 Sol,Codex CLI,codex-cli 0.157.1,high,fast,0,5,17,100975,2.77,150400,9276544,61839,scored,policies/wall_cupboard_open/gpt6sol-s3/policy.py
182
+ dishwasher_close,core,gpt61sol-s1,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,36,157,100835,3.21,403847,14552576,94590,scored,policies/dishwasher_close/gpt61sol-s1/policy.py
183
+ dishwasher_close,core,gpt61sol-s2,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,5,78,100981,2.09,254597,8151936,76960,scored,policies/dishwasher_close/gpt61sol-s2/policy.py
184
+ dishwasher_close,core,gpt61sol-s3,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,5,105,100633,2.06,202973,10248832,63385,scored,policies/dishwasher_close/gpt61sol-s3/policy.py
185
+ dishwasher_load_cups,core,gpt61sol-s1,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,36,8,77,100347,2.14,288298,9871488,57553,scored,policies/dishwasher_load_cups/gpt61sol-s1/policy.py
186
+ dishwasher_load_cups,core,gpt61sol-s2,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,2,19,120,100802,1.64,133539,7979264,57502,scored,policies/dishwasher_load_cups/gpt61sol-s2/policy.py
187
+ dishwasher_load_cups,core,gpt61sol-s3,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,15,17,125,100795,2.65,227970,14738560,71873,scored,policies/dishwasher_load_cups/gpt61sol-s3/policy.py
188
+ drawer_top_close,core,gpt61sol-s1,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,100,2,19,48565,0.25,53328,903552,5105,scored,policies/drawer_top_close/gpt61sol-s1/policy.py
189
+ drawer_top_close,core,gpt61sol-s2,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,100,2,22,96213,0.3,57390,1171072,6555,scored,policies/drawer_top_close/gpt61sol-s2/policy.py
190
+ drawer_top_close,core,gpt61sol-s3,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,100,1,41,60843,0.34,57363,1253760,10196,scored,policies/drawer_top_close/gpt61sol-s3/policy.py
191
+ drawer_top_open,core,gpt61sol-s1,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,40,13,98,100982,0.82,125696,3659520,20308,scored,policies/drawer_top_open/gpt61sol-s1/policy.py
192
+ drawer_top_open,core,gpt61sol-s2,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,100,3,66,88613,0.74,98223,3186688,22662,scored,policies/drawer_top_open/gpt61sol-s2/policy.py
193
+ drawer_top_open,core,gpt61sol-s3,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,34,15,34,101000,1.25,181377,5928320,29194,scored,policies/drawer_top_open/gpt61sol-s3/policy.py
194
+ move_plate,core,gpt61sol-s1,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,7,4,99,100932,0.96,103511,4179712,33695,scored,policies/move_plate/gpt61sol-s1/policy.py
195
+ move_plate,core,gpt61sol-s2,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,2,151,100960,0.95,103347,3928192,34967,scored,policies/move_plate/gpt61sol-s2/policy.py
196
+ move_plate,core,gpt61sol-s3,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,17,7,38,101000,0.73,87638,2978176,25286,scored,policies/move_plate/gpt61sol-s3/policy.py
197
+ move_two_plates,core,gpt61sol-s1,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,17,153,100954,2.28,227069,10540800,76836,scored,policies/move_two_plates/gpt61sol-s1/policy.py
198
+ move_two_plates,core,gpt61sol-s2,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,8,22,144,101000,1.57,152675,7315456,53682,scored,policies/move_two_plates/gpt61sol-s2/policy.py
199
+ move_two_plates,core,gpt61sol-s3,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,34,67,100991,2.15,196180,11639296,58872,scored,policies/move_two_plates/gpt61sol-s3/policy.py
200
+ pick_box,core,gpt61sol-s1,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,2,94,100945,0.88,98715,4099840,27470,scored,policies/pick_box/gpt61sol-s1/policy.py
201
+ pick_box,core,gpt61sol-s2,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,52,14,92,101000,1.23,162806,5624576,34502,scored,policies/pick_box/gpt61sol-s2/policy.py
202
+ pick_box,core,gpt61sol-s3,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,2,172,100492,2.33,187656,13785088,57426,scored,policies/pick_box/gpt61sol-s3/policy.py
203
+ reach_target_dual,core,gpt61sol-s1,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,82,12,170,101000,1.09,130332,3925504,44108,scored,policies/reach_target_dual/gpt61sol-s1/policy.py
204
+ reach_target_dual,core,gpt61sol-s2,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,61,13,140,100760,0.76,103112,2719104,28392,scored,policies/reach_target_dual/gpt61sol-s2/policy.py
205
+ reach_target_dual,core,gpt61sol-s3,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,66,9,113,99489,0.58,81897,2347776,18214,scored,policies/reach_target_dual/gpt61sol-s3/policy.py
206
+ reach_target_multi_modal,core,gpt61sol-s1,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,81,11,153,100861,0.93,116455,3386240,35549,scored,policies/reach_target_multi_modal/gpt61sol-s1/policy.py
207
+ reach_target_multi_modal,core,gpt61sol-s2,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,97,9,152,100752,1.07,179517,3396608,37570,scored,policies/reach_target_multi_modal/gpt61sol-s2/policy.py
208
+ reach_target_multi_modal,core,gpt61sol-s3,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,100,14,152,100045,0.66,86235,3200128,17112,scored,policies/reach_target_multi_modal/gpt61sol-s3/policy.py
209
+ dishwasher_load_cutlery,extended,gpt61sol-s1,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,21,106,100947,2.45,208559,12400768,79521,scored,policies/dishwasher_load_cutlery/gpt61sol-s1/policy.py
210
+ dishwasher_load_cutlery,extended,gpt61sol-s2,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,12,91,101000,4.03,441517,21943680,94872,scored,policies/dishwasher_load_cutlery/gpt61sol-s2/policy.py
211
+ dishwasher_load_cutlery,extended,gpt61sol-s3,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,11,58,100971,2.22,280522,8884224,77315,scored,policies/dishwasher_load_cutlery/gpt61sol-s3/policy.py
212
+ dishwasher_load_plates,extended,gpt61sol-s1,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,7,98,100875,2.12,186222,10526336,69453,scored,policies/dishwasher_load_plates/gpt61sol-s1/policy.py
213
+ dishwasher_load_plates,extended,gpt61sol-s2,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,8,132,100854,2.08,179783,11737472,54435,scored,policies/dishwasher_load_plates/gpt61sol-s2/policy.py
214
+ dishwasher_load_plates,extended,gpt61sol-s3,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,2,5,94,100797,2.41,180521,13691776,68193,scored,policies/dishwasher_load_plates/gpt61sol-s3/policy.py
215
+ flip_cup,extended,gpt61sol-s1,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,23,12,137,100051,1.15,172849,4176384,38683,scored,policies/flip_cup/gpt61sol-s1/policy.py
216
+ flip_cup,extended,gpt61sol-s2,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,51,16,100,101000,1.69,258739,6592768,51194,scored,policies/flip_cup/gpt61sol-s2/policy.py
217
+ flip_cup,extended,gpt61sol-s3,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,4,70,100916,1.44,150848,6722688,46738,scored,policies/flip_cup/gpt61sol-s3/policy.py
218
+ flip_cutlery,extended,gpt61sol-s1,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,1,11,115,100998,2.16,187832,12233728,56551,scored,policies/flip_cutlery/gpt61sol-s1/policy.py
219
+ flip_cutlery,extended,gpt61sol-s2,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,12,9,95,101000,2.92,239592,17344640,70835,scored,policies/flip_cutlery/gpt61sol-s2/policy.py
220
+ flip_cutlery,extended,gpt61sol-s3,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,1,7,305,100626,2.24,217621,11378816,67081,scored,policies/flip_cutlery/gpt61sol-s3/policy.py
221
+ put_cups,extended,gpt61sol-s1,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,7,155,100990,4.49,611624,22465664,102164,scored,policies/put_cups/gpt61sol-s1/policy.py
222
+ put_cups,extended,gpt61sol-s2,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,15,182,100980,3.17,341232,13551744,112939,scored,policies/put_cups/gpt61sol-s2/policy.py
223
+ put_cups,extended,gpt61sol-s3,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,18,169,101000,3.36,570837,11331072,108346,scored,policies/put_cups/gpt61sol-s3/policy.py
224
+ reach_target_single,extended,gpt61sol-s1,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,84,9,135,100774,1.23,133084,5763328,38581,scored,policies/reach_target_single/gpt61sol-s1/policy.py
225
+ reach_target_single,extended,gpt61sol-s2,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,97,6,68,100707,0.72,95406,3347072,19761,scored,policies/reach_target_single/gpt61sol-s2/policy.py
226
+ reach_target_single,extended,gpt61sol-s3,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,97,9,115,100449,0.7,75292,2892672,25897,scored,policies/reach_target_single/gpt61sol-s3/policy.py
227
+ sandwich_remove,extended,gpt61sol-s1,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,4,78,100969,1.44,170874,5327616,56837,scored,policies/sandwich_remove/gpt61sol-s1/policy.py
228
+ sandwich_remove,extended,gpt61sol-s2,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,8,232,100977,5.8,576813,32899968,136127,scored,policies/sandwich_remove/gpt61sol-s2/policy.py
229
+ sandwich_remove,extended,gpt61sol-s3,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,1,6,81,100933,3.41,503712,16425600,76320,scored,policies/sandwich_remove/gpt61sol-s3/policy.py
230
+ saucepan_to_hob,extended,gpt61sol-s1,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,75,12,117,100167,2.41,191107,15502976,47427,scored,policies/saucepan_to_hob/gpt61sol-s1/policy.py
231
+ saucepan_to_hob,extended,gpt61sol-s2,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,19,107,100974,7.23,680154,42682624,160635,scored,policies/saucepan_to_hob/gpt61sol-s2/policy.py
232
+ saucepan_to_hob,extended,gpt61sol-s3,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,13,85,100804,5.08,544349,28178688,117573,scored,policies/saucepan_to_hob/gpt61sol-s3/policy.py
233
+ stack_blocks,extended,gpt61sol-s1,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,19,207,100845,1.25,129108,5636096,42450,scored,policies/stack_blocks/gpt61sol-s1/policy.py
234
+ stack_blocks,extended,gpt61sol-s2,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,1,8,123,100909,1.29,175139,5038464,43971,scored,policies/stack_blocks/gpt61sol-s2/policy.py
235
+ stack_blocks,extended,gpt61sol-s3,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,0,18,162,100988,3.53,395041,19723136,77116,scored,policies/stack_blocks/gpt61sol-s3/policy.py
236
+ wall_cupboard_close,extended,gpt61sol-s1,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,100,2,34,53040,0.38,102671,1021056,6882,scored,policies/wall_cupboard_close/gpt61sol-s1/policy.py
237
+ wall_cupboard_close,extended,gpt61sol-s2,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,100,1,36,74285,0.37,62738,1461760,10092,scored,policies/wall_cupboard_close/gpt61sol-s2/policy.py
238
+ wall_cupboard_close,extended,gpt61sol-s3,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,100,1,44,77046,0.61,94356,2283776,19105,scored,policies/wall_cupboard_close/gpt61sol-s3/policy.py
239
+ wall_cupboard_open,extended,gpt61sol-s1,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,100,9,92,100268,1.4,150676,6042496,49511,scored,policies/wall_cupboard_open/gpt61sol-s1/policy.py
240
+ wall_cupboard_open,extended,gpt61sol-s2,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,100,16,59,100544,1.52,159271,7363456,46579,scored,policies/wall_cupboard_open/gpt61sol-s2/policy.py
241
+ wall_cupboard_open,extended,gpt61sol-s3,gpt61sol,GPT-6.1 Sol,Codex CLI,codex-cli 0.160.0,high,default,100,23,67,100806,2.97,542669,10807040,80620,scored,policies/wall_cupboard_open/gpt61sol-s3/policy.py
242
+ dishwasher_close,core,kimi31pro-s1,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,3,172,99479,25.84,2638836,92998447,196433,scored,policies/dishwasher_close/kimi31pro-s1/policy.py
243
+ dishwasher_close,core,kimi31pro-s2,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,44,107,100834,12.87,1465462,41783683,158143,scored,policies/dishwasher_close/kimi31pro-s2/policy.py
244
+ dishwasher_close,core,kimi31pro-s3,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,21,103,97720,10.08,1367379,32126991,92343,scored,policies/dishwasher_close/kimi31pro-s3/policy.py
245
+ dishwasher_load_cups,core,kimi31pro-s1,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,4,51,258,99173,7.81,1048849,22731868,116620,scored,policies/dishwasher_load_cups/kimi31pro-s1/policy.py
246
+ dishwasher_load_cups,core,kimi31pro-s2,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,2,120,100826,10.32,1374067,31828546,121017,scored,policies/dishwasher_load_cups/kimi31pro-s2/policy.py
247
+ dishwasher_load_cups,core,kimi31pro-s3,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,6,191,100984,33.9,2141360,135555878,250753,scored,policies/dishwasher_load_cups/kimi31pro-s3/policy.py
248
+ drawer_top_close,core,kimi31pro-s1,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,100,10,37,47126,1.78,171714,5251772,38632,scored,policies/drawer_top_close/kimi31pro-s1/policy.py
249
+ drawer_top_close,core,kimi31pro-s2,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,22,1,93,100990,4.2,477519,11684633,90526,scored,policies/drawer_top_close/kimi31pro-s2/policy.py
250
+ drawer_top_close,core,kimi31pro-s3,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,47,2,12,27480,2.54,344588,7268434,39595,scored,policies/drawer_top_close/kimi31pro-s3/policy.py
251
+ drawer_top_open,core,kimi31pro-s1,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,1,143,100160,21.51,1624586,82815037,170160,scored,policies/drawer_top_open/kimi31pro-s1/policy.py
252
+ drawer_top_open,core,kimi31pro-s2,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,84,16,178,100740,7.75,1072686,23228374,95990,scored,policies/drawer_top_open/kimi31pro-s2/policy.py
253
+ drawer_top_open,core,kimi31pro-s3,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,16,2,67,100090,13.82,1189909,48440339,175430,scored,policies/drawer_top_open/kimi31pro-s3/policy.py
254
+ move_plate,core,kimi31pro-s1,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,2,169,100456,12.53,1376250,41738450,142781,scored,policies/move_plate/kimi31pro-s1/policy.py
255
+ move_plate,core,kimi31pro-s2,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,6,145,101000,10.54,2195146,25647472,102360,scored,policies/move_plate/kimi31pro-s2/policy.py
256
+ move_plate,core,kimi31pro-s3,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,4,137,100505,6.37,692320,19870710,100816,scored,policies/move_plate/kimi31pro-s3/policy.py
257
+ move_two_plates,core,kimi31pro-s1,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,2,123,100982,19.54,1571399,74601158,147398,scored,policies/move_two_plates/kimi31pro-s1/policy.py
258
+ move_two_plates,core,kimi31pro-s2,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,3,113,99933,11.74,993052,42926281,116768,scored,policies/move_two_plates/kimi31pro-s2/policy.py
259
+ move_two_plates,core,kimi31pro-s3,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,14,165,75520,20.12,7076912,29820431,0,scored,policies/move_two_plates/kimi31pro-s3/policy.py
260
+ pick_box,core,kimi31pro-s1,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,2,175,101000,11.12,1343697,35017296,143024,scored,policies/pick_box/kimi31pro-s1/policy.py
261
+ pick_box,core,kimi31pro-s2,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,2,302,99306,17.65,1551173,65500939,144739,scored,policies/pick_box/kimi31pro-s2/policy.py
262
+ pick_box,core,kimi31pro-s3,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,2,196,100770,10.07,1063395,33441344,125604,scored,policies/pick_box/kimi31pro-s3/policy.py
263
+ reach_target_dual,core,kimi31pro-s1,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,1,176,100996,18.23,578494,75117705,205298,scored,policies/reach_target_dual/kimi31pro-s1/policy.py
264
+ reach_target_dual,core,kimi31pro-s2,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,7,2,109,100743,8.68,272686,34333818,126815,scored,policies/reach_target_dual/kimi31pro-s2/policy.py
265
+ reach_target_dual,core,kimi31pro-s3,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,48,18,157,96196,10.13,1624501,29532872,97779,scored,policies/reach_target_dual/kimi31pro-s3/policy.py
266
+ reach_target_multi_modal,core,kimi31pro-s1,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,100,2,133,61407,3.24,237188,10691400,63080,scored,policies/reach_target_multi_modal/kimi31pro-s1/policy.py
267
+ reach_target_multi_modal,core,kimi31pro-s2,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,59,21,134,100592,7.67,698839,24789431,131623,scored,policies/reach_target_multi_modal/kimi31pro-s2/policy.py
268
+ reach_target_multi_modal,core,kimi31pro-s3,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,87,18,151,90814,7.98,1040095,24828078,93330,scored,policies/reach_target_multi_modal/kimi31pro-s3/policy.py
269
+ dishwasher_load_cutlery,extended,kimi31pro-s1,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,,,821,0.1918,42861,219392,6218,"void: the submission is the untouched template, byte for byte",
270
+ dishwasher_load_cutlery,extended,kimi31pro-s2,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,1,3,147,100785,12.91,1447603,43324273,134620,scored,policies/dishwasher_load_cutlery/kimi31pro-s2/policy.py
271
+ dishwasher_load_cutlery,extended,kimi31pro-s3,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,2,4,97,100494,8.18,1449313,21729425,93508,scored,policies/dishwasher_load_cutlery/kimi31pro-s3/policy.py
272
+ dishwasher_load_plates,extended,kimi31pro-s1,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,58,147,99711,15.27,1417903,54830037,146869,scored,policies/dishwasher_load_plates/kimi31pro-s1/policy.py
273
+ dishwasher_load_plates,extended,kimi31pro-s2,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,5,159,100974,13.19,1323105,46738979,119289,scored,policies/dishwasher_load_plates/kimi31pro-s2/policy.py
274
+ dishwasher_load_plates,extended,kimi31pro-s3,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,24,182,100972,14.39,1497896,51333817,112691,scored,policies/dishwasher_load_plates/kimi31pro-s3/policy.py
275
+ flip_cup,extended,kimi31pro-s1,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,16,24,251,100424,23.04,2264885,82605124,199038,scored,policies/flip_cup/kimi31pro-s1/policy.py
276
+ flip_cup,extended,kimi31pro-s2,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,4,119,100526,8.48,1449193,22994743,98569,scored,policies/flip_cup/kimi31pro-s2/policy.py
277
+ flip_cup,extended,kimi31pro-s3,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,6,171,100360,16.16,1593746,55952345,178175,scored,policies/flip_cup/kimi31pro-s3/policy.py
278
+ flip_cutlery,extended,kimi31pro-s1,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,3,77,101000,18.33,1616796,67391437,161848,scored,policies/flip_cutlery/kimi31pro-s1/policy.py
279
+ flip_cutlery,extended,kimi31pro-s2,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,2,157,100032,16.57,2342684,52061947,147298,scored,policies/flip_cutlery/kimi31pro-s2/policy.py
280
+ flip_cutlery,extended,kimi31pro-s3,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,4,213,100904,14.76,1638935,52125966,105456,scored,policies/flip_cutlery/kimi31pro-s3/policy.py
281
+ put_cups,extended,kimi31pro-s1,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,4,160,101000,21.58,2156299,76760386,191187,scored,policies/put_cups/kimi31pro-s1/policy.py
282
+ put_cups,extended,kimi31pro-s2,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,15,221,100964,33.21,2265763,133290768,201760,scored,policies/put_cups/kimi31pro-s2/policy.py
283
+ put_cups,extended,kimi31pro-s3,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,,,82240,19.5631,2451637,73299331,0,"void: the submission is the untouched template, byte for byte",
284
+ reach_target_single,extended,kimi31pro-s1,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,55,19,98,100554,5.68,468778,18708216,100362,scored,policies/reach_target_single/kimi31pro-s1/policy.py
285
+ reach_target_single,extended,kimi31pro-s2,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,82,14,97,101000,4.02,722904,10301131,51849,scored,policies/reach_target_single/kimi31pro-s2/policy.py
286
+ reach_target_single,extended,kimi31pro-s3,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,77,41,256,100787,6.74,519030,23842406,93519,scored,policies/reach_target_single/kimi31pro-s3/policy.py
287
+ sandwich_remove,extended,kimi31pro-s1,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,,,100441,37.3048,3360394,144788284,162634,void: no scorable program,
288
+ sandwich_remove,extended,kimi31pro-s2,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,3,82,100779,20.24,2248109,71117595,152507,scored,policies/sandwich_remove/kimi31pro-s2/policy.py
289
+ sandwich_remove,extended,kimi31pro-s3,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,,,23434,10.1799,964302,36613602,92855,"void: the submission is the untouched template, byte for byte",
290
+ saucepan_to_hob,extended,kimi31pro-s1,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,,,89074,19.8253,1996139,79165191,0,"void: the submission is the untouched template, byte for byte",
291
+ saucepan_to_hob,extended,kimi31pro-s2,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,,,52447,31.0943,2849385,126977835,0,"void: the submission is the untouched template, byte for byte",
292
+ saucepan_to_hob,extended,kimi31pro-s3,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,4,174,101000,19.42,1539782,73384258,165937,scored,policies/saucepan_to_hob/kimi31pro-s3/policy.py
293
+ stack_blocks,extended,kimi31pro-s1,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,20,379,100980,15.31,1857221,52029868,118615,scored,policies/stack_blocks/kimi31pro-s1/policy.py
294
+ stack_blocks,extended,kimi31pro-s2,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,10,243,100913,11.39,994472,41168132,116345,scored,policies/stack_blocks/kimi31pro-s2/policy.py
295
+ stack_blocks,extended,kimi31pro-s3,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,20,247,100754,9.52,1438339,26497118,134111,scored,policies/stack_blocks/kimi31pro-s3/policy.py
296
+ wall_cupboard_close,extended,kimi31pro-s1,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,100,3,87,101000,10.77,1449393,32198873,143605,scored,policies/wall_cupboard_close/kimi31pro-s1/policy.py
297
+ wall_cupboard_close,extended,kimi31pro-s2,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,100,3,107,99527,14.37,1372206,51855060,125606,scored,policies/wall_cupboard_close/kimi31pro-s2/policy.py
298
+ wall_cupboard_close,extended,kimi31pro-s3,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,5,60,100150,13.85,474662,59318197,103372,scored,policies/wall_cupboard_close/kimi31pro-s3/policy.py
299
+ wall_cupboard_open,extended,kimi31pro-s1,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,5,211,100988,24.33,1806666,94241616,186347,scored,policies/wall_cupboard_open/kimi31pro-s1/policy.py
300
+ wall_cupboard_open,extended,kimi31pro-s2,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,100,2,130,100665,21.41,1649981,83226309,146548,scored,policies/wall_cupboard_open/kimi31pro-s2/policy.py
301
+ wall_cupboard_open,extended,kimi31pro-s3,kimi31pro,Kimi K3.1 Pro,Claude Code,2.1.280 (Claude Code),max,default,0,10,113,100691,19.98,1683608,74864319,163777,scored,policies/wall_cupboard_open/kimi31pro-s3/policy.py
302
+ dishwasher_close,core,deepseek1017-s1,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,5,201,101000,0.39,204589,69002240,255874,scored,policies/dishwasher_close/deepseek1017-s1/policy.py
303
+ dishwasher_close,core,deepseek1017-s2,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,11,230,94732,0.37,184656,57161216,281094,scored,policies/dishwasher_close/deepseek1017-s2/policy.py
304
+ dishwasher_close,core,deepseek1017-s3,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,4,246,100713,0.35,146458,59107200,258764,scored,policies/dishwasher_close/deepseek1017-s3/policy.py
305
+ dishwasher_load_cups,core,deepseek1017-s1,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,5,111,100884,0.54,226126,98663040,346425,scored,policies/dishwasher_load_cups/deepseek1017-s1/policy.py
306
+ dishwasher_load_cups,core,deepseek1017-s2,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,13,168,100957,0.42,198063,70754688,295829,scored,policies/dishwasher_load_cups/deepseek1017-s2/policy.py
307
+ dishwasher_load_cups,core,deepseek1017-s3,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,5,157,99643,0.42,171957,66204928,321972,scored,policies/dishwasher_load_cups/deepseek1017-s3/policy.py
308
+ drawer_top_close,core,deepseek1017-s1,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,99,6,112,100839,0.1,84497,8438400,105770,scored,policies/drawer_top_close/deepseek1017-s1/policy.py
309
+ drawer_top_close,core,deepseek1017-s2,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,91,4,141,94881,0.29,126286,35281920,277891,scored,policies/drawer_top_close/deepseek1017-s2/policy.py
310
+ drawer_top_close,core,deepseek1017-s3,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,99,1,45,75732,0.17,115825,20380800,157577,scored,policies/drawer_top_close/deepseek1017-s3/policy.py
311
+ drawer_top_open,core,deepseek1017-s1,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,5,174,100980,0.28,145982,36591872,252743,scored,policies/drawer_top_open/deepseek1017-s1/policy.py
312
+ drawer_top_open,core,deepseek1017-s2,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,4,187,101000,0.32,178503,44942848,256281,scored,policies/drawer_top_open/deepseek1017-s2/policy.py
313
+ drawer_top_open,core,deepseek1017-s3,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,3,197,100642,0.34,164202,52971518,265152,scored,policies/drawer_top_open/deepseek1017-s3/policy.py
314
+ move_plate,core,deepseek1017-s1,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,5,211,101000,0.4,207672,62723840,303746,scored,policies/move_plate/deepseek1017-s1/policy.py
315
+ move_plate,core,deepseek1017-s2,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,5,182,100180,0.49,567149,76743552,298932,scored,policies/move_plate/deepseek1017-s2/policy.py
316
+ move_plate,core,deepseek1017-s3,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,3,133,96151,0.4,186296,67842048,274474,scored,policies/move_plate/deepseek1017-s3/policy.py
317
+ move_two_plates,core,deepseek1017-s1,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,3,270,76987,0.45,327873,85036416,241065,scored,policies/move_two_plates/deepseek1017-s1/policy.py
318
+ move_two_plates,core,deepseek1017-s2,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,,,40703,0.2219,152753,26107648,201073,"void: the submission is the untouched template, byte for byte",
319
+ move_two_plates,core,deepseek1017-s3,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,3,145,68182,0.38,198103,61976960,266211,scored,policies/move_two_plates/deepseek1017-s3/policy.py
320
+ pick_box,core,deepseek1017-s1,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,5,219,99654,0.38,188495,54425472,313074,scored,policies/pick_box/deepseek1017-s1/policy.py
321
+ pick_box,core,deepseek1017-s2,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,12,393,98395,0.28,182161,32797056,261412,scored,policies/pick_box/deepseek1017-s2/policy.py
322
+ pick_box,core,deepseek1017-s3,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,,,7501,0.0389,44047,3133952,38175,"void: the submission is the untouched template, byte for byte",
323
+ reach_target_dual,core,deepseek1017-s1,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,5,190,98640,0.36,134260,44014208,342111,scored,policies/reach_target_dual/deepseek1017-s1/policy.py
324
+ reach_target_dual,core,deepseek1017-s2,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,20,14,268,99709,0.24,110742,23923200,258267,scored,policies/reach_target_dual/deepseek1017-s2/policy.py
325
+ reach_target_dual,core,deepseek1017-s3,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,35,8,124,100008,0.33,178787,43235456,296723,scored,policies/reach_target_dual/deepseek1017-s3/policy.py
326
+ reach_target_multi_modal,core,deepseek1017-s1,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,63,721,0,0.3,118122,40599808,274976,scored,policies/reach_target_multi_modal/deepseek1017-s1/policy.py
327
+ reach_target_multi_modal,core,deepseek1017-s2,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,10,26,862,0,0.19,82518,17546880,215648,scored,policies/reach_target_multi_modal/deepseek1017-s2/policy.py
328
+ reach_target_multi_modal,core,deepseek1017-s3,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,1,35,814,0,0.19,118697,19942784,194487,scored,policies/reach_target_multi_modal/deepseek1017-s3/policy.py
329
+ dishwasher_load_cutlery,extended,deepseek1017-s1,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,50,573,0,0.25,147283,31369472,217120,scored,policies/dishwasher_load_cutlery/deepseek1017-s1/policy.py
330
+ dishwasher_load_cutlery,extended,deepseek1017-s2,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,27,822,0,0.23,104908,23742208,240090,scored,policies/dishwasher_load_cutlery/deepseek1017-s2/policy.py
331
+ dishwasher_load_cutlery,extended,deepseek1017-s3,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,32,355,0,0.28,131116,42646912,224315,scored,policies/dishwasher_load_cutlery/deepseek1017-s3/policy.py
332
+ dishwasher_load_plates,extended,deepseek1017-s1,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,20,405,0,0.16,111802,17052288,145445,scored,policies/dishwasher_load_plates/deepseek1017-s1/policy.py
333
+ dishwasher_load_plates,extended,deepseek1017-s2,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,28,664,0,0.17,106315,14820992,180846,scored,policies/dishwasher_load_plates/deepseek1017-s2/policy.py
334
+ dishwasher_load_plates,extended,deepseek1017-s3,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,15,308,0,0.2,101588,24100608,182959,scored,policies/dishwasher_load_plates/deepseek1017-s3/policy.py
335
+ flip_cup,extended,deepseek1017-s1,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,1,220,89712,0.36,198883,57252992,269053,scored,policies/flip_cup/deepseek1017-s1/policy.py
336
+ flip_cup,extended,deepseek1017-s2,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,5,289,101000,0.37,153283,46425344,338559,scored,policies/flip_cup/deepseek1017-s2/policy.py
337
+ flip_cup,extended,deepseek1017-s3,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,4,364,98663,0.43,208183,79748608,261940,scored,policies/flip_cup/deepseek1017-s3/policy.py
338
+ flip_cutlery,extended,deepseek1017-s1,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,4,123,100834,0.37,176180,67763840,237990,scored,policies/flip_cutlery/deepseek1017-s1/policy.py
339
+ flip_cutlery,extended,deepseek1017-s2,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,12,376,84990,0.33,179809,50946560,258173,scored,policies/flip_cutlery/deepseek1017-s2/policy.py
340
+ flip_cutlery,extended,deepseek1017-s3,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,9,275,69176,0.34,153510,48504960,280593,scored,policies/flip_cutlery/deepseek1017-s3/policy.py
341
+ put_cups,extended,deepseek1017-s1,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,4,211,97878,0.41,192879,69361920,293616,scored,policies/put_cups/deepseek1017-s1/policy.py
342
+ put_cups,extended,deepseek1017-s2,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,13,175,97890,0.53,185285,92579328,374570,scored,policies/put_cups/deepseek1017-s2/policy.py
343
+ put_cups,extended,deepseek1017-s3,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,4,160,99516,0.33,159376,52464512,254944,scored,policies/put_cups/deepseek1017-s3/policy.py
344
+ reach_target_single,extended,deepseek1017-s1,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,61,6,279,101000,0.22,157419,25130624,195171,scored,policies/reach_target_single/deepseek1017-s1/policy.py
345
+ reach_target_single,extended,deepseek1017-s2,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,24,17,203,99324,0.42,220768,58817792,346202,scored,policies/reach_target_single/deepseek1017-s2/policy.py
346
+ reach_target_single,extended,deepseek1017-s3,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,96,7,193,100791,0.17,125264,14373248,184587,scored,policies/reach_target_single/deepseek1017-s3/policy.py
347
+ sandwich_remove,extended,deepseek1017-s1,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,3,191,100534,0.33,150285,46318848,280017,scored,policies/sandwich_remove/deepseek1017-s1/policy.py
348
+ sandwich_remove,extended,deepseek1017-s2,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,2,162,95147,0.55,232599,100829824,362609,scored,policies/sandwich_remove/deepseek1017-s2/policy.py
349
+ sandwich_remove,extended,deepseek1017-s3,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,10,287,91722,0.36,213255,56174976,258989,scored,policies/sandwich_remove/deepseek1017-s3/policy.py
350
+ saucepan_to_hob,extended,deepseek1017-s1,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,5,357,100792,0.4,176679,70610176,274608,scored,policies/saucepan_to_hob/deepseek1017-s1/policy.py
351
+ saucepan_to_hob,extended,deepseek1017-s2,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,4,292,99505,0.41,193278,68477440,289438,scored,policies/saucepan_to_hob/deepseek1017-s2/policy.py
352
+ saucepan_to_hob,extended,deepseek1017-s3,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,7,299,99986,0.4,225951,62242304,293726,scored,policies/saucepan_to_hob/deepseek1017-s3/policy.py
353
+ stack_blocks,extended,deepseek1017-s1,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,7,311,101000,0.25,130863,30753152,235879,scored,policies/stack_blocks/deepseek1017-s1/policy.py
354
+ stack_blocks,extended,deepseek1017-s2,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,5,303,100764,0.28,155075,36112512,252010,scored,policies/stack_blocks/deepseek1017-s2/policy.py
355
+ stack_blocks,extended,deepseek1017-s3,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,11,236,97363,0.33,143559,48217728,268558,scored,policies/stack_blocks/deepseek1017-s3/policy.py
356
+ wall_cupboard_close,extended,deepseek1017-s1,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,3,149,100068,0.36,172993,47115776,322495,scored,policies/wall_cupboard_close/deepseek1017-s1/policy.py
357
+ wall_cupboard_close,extended,deepseek1017-s2,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,100,1,122,93791,0.34,152725,51454464,263385,scored,policies/wall_cupboard_close/deepseek1017-s2/policy.py
358
+ wall_cupboard_close,extended,deepseek1017-s3,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,3,182,95457,0.43,189866,60228352,370109,scored,policies/wall_cupboard_close/deepseek1017-s3/policy.py
359
+ wall_cupboard_open,extended,deepseek1017-s1,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,5,225,100539,0.47,206259,76819968,342657,scored,policies/wall_cupboard_open/deepseek1017-s1/policy.py
360
+ wall_cupboard_open,extended,deepseek1017-s2,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,4,119,86678,0.57,287459,105897728,354574,scored,policies/wall_cupboard_open/deepseek1017-s2/policy.py
361
+ wall_cupboard_open,extended,deepseek1017-s3,deepseek1017,DeepSeek V4.1 Flash (1017),DeepSeek CLI (dsh),0.2.1-rc.2,max,default,0,6,119,101000,0.46,191728,72174976,358948,scored,policies/wall_cupboard_open/deepseek1017-s3/policy.py
362
+ dishwasher_close,core,gpt61terra-s1,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,3,54,98701,0.68,223622,6669056,25348,scored,policies/dishwasher_close/gpt61terra-s1/policy.py
363
+ dishwasher_close,core,gpt61terra-s2,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,2,74,96350,1.07,185708,14334720,32671,scored,policies/dishwasher_close/gpt61terra-s2/policy.py
364
+ dishwasher_close,core,gpt61terra-s3,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,4,47,100780,0.96,104394,13574144,34402,scored,policies/dishwasher_close/gpt61terra-s3/policy.py
365
+ dishwasher_load_cups,core,gpt61terra-s1,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,1,68,30927,0.49,106260,6052992,16980,scored,policies/dishwasher_load_cups/gpt61terra-s1/policy.py
366
+ dishwasher_load_cups,core,gpt61terra-s2,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,3,44,34530,0.47,115270,5379712,17328,scored,policies/dishwasher_load_cups/gpt61terra-s2/policy.py
367
+ dishwasher_load_cups,core,gpt61terra-s3,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,36,7,43,87426,1.12,211917,15364864,28831,scored,policies/dishwasher_load_cups/gpt61terra-s3/policy.py
368
+ drawer_top_close,core,gpt61terra-s1,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,70,4,17,101000,0.24,66134,2510464,10387,scored,policies/drawer_top_close/gpt61terra-s1/policy.py
369
+ drawer_top_close,core,gpt61terra-s2,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,88,6,16,97369,0.27,67925,3137920,8580,scored,policies/drawer_top_close/gpt61terra-s2/policy.py
370
+ drawer_top_close,core,gpt61terra-s3,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,81,5,15,97928,0.25,49107,3182464,9257,scored,policies/drawer_top_close/gpt61terra-s3/policy.py
371
+ drawer_top_open,core,gpt61terra-s1,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,100,4,37,100418,0.47,128277,4772864,19997,scored,policies/drawer_top_open/gpt61terra-s1/policy.py
372
+ drawer_top_open,core,gpt61terra-s2,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,2,37,100934,0.5,87065,6531072,17661,scored,policies/drawer_top_open/gpt61terra-s2/policy.py
373
+ drawer_top_open,core,gpt61terra-s3,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,27,5,46,101000,0.83,177732,10476928,25999,scored,policies/drawer_top_open/gpt61terra-s3/policy.py
374
+ move_plate,core,gpt61terra-s1,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,3,2,38,100751,0.46,98398,5232768,19043,scored,policies/move_plate/gpt61terra-s1/policy.py
375
+ move_plate,core,gpt61terra-s2,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,2,56,98241,0.8,116997,10929280,27762,scored,policies/move_plate/gpt61terra-s2/policy.py
376
+ move_plate,core,gpt61terra-s3,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,1,6,31,99847,0.66,91464,8794880,25468,scored,policies/move_plate/gpt61terra-s3/policy.py
377
+ move_two_plates,core,gpt61terra-s1,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,1,59,100920,0.58,112054,7130624,22364,scored,policies/move_two_plates/gpt61terra-s1/policy.py
378
+ move_two_plates,core,gpt61terra-s2,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,3,70,98195,0.98,168479,13447040,27076,scored,policies/move_two_plates/gpt61terra-s2/policy.py
379
+ move_two_plates,core,gpt61terra-s3,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,5,11,52,99784,1.03,145823,14936320,27246,scored,policies/move_two_plates/gpt61terra-s3/policy.py
380
+ pick_box,core,gpt61terra-s1,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,3,4,37,101000,0.79,153827,10743040,20264,scored,policies/pick_box/gpt61terra-s1/policy.py
381
+ pick_box,core,gpt61terra-s2,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,8,6,35,100837,0.67,119766,8539776,24597,scored,policies/pick_box/gpt61terra-s2/policy.py
382
+ pick_box,core,gpt61terra-s3,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,6,110,94000,1.19,176197,17336320,30040,scored,policies/pick_box/gpt61terra-s3/policy.py
383
+ reach_target_dual,core,gpt61terra-s1,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,67,7,54,96185,0.44,137351,4476672,15883,scored,policies/reach_target_dual/gpt61terra-s1/policy.py
384
+ reach_target_dual,core,gpt61terra-s2,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,34,6,57,98847,0.72,131414,8145920,35830,scored,policies/reach_target_dual/gpt61terra-s2/policy.py
385
+ reach_target_dual,core,gpt61terra-s3,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,34,8,63,100905,0.66,171551,7149184,26445,scored,policies/reach_target_dual/gpt61terra-s3/policy.py
386
+ reach_target_multi_modal,core,gpt61terra-s1,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,86,7,66,100097,0.42,101419,4562560,17918,scored,policies/reach_target_multi_modal/gpt61terra-s1/policy.py
387
+ reach_target_multi_modal,core,gpt61terra-s2,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,84,6,28,100916,0.48,69440,6277248,19104,scored,policies/reach_target_multi_modal/gpt61terra-s2/policy.py
388
+ reach_target_multi_modal,core,gpt61terra-s3,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,46,10,45,100242,0.56,116136,6442624,23939,scored,policies/reach_target_multi_modal/gpt61terra-s3/policy.py
389
+ dishwasher_load_cutlery,extended,gpt61terra-s1,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,1,3,71,82391,0.98,156026,13393280,31086,scored,policies/dishwasher_load_cutlery/gpt61terra-s1/policy.py
390
+ dishwasher_load_cutlery,extended,gpt61terra-s2,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,2,48,21947,0.29,70957,2932864,13523,scored,policies/dishwasher_load_cutlery/gpt61terra-s2/policy.py
391
+ dishwasher_load_cutlery,extended,gpt61terra-s3,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,2,67,99850,1.36,379275,15532288,40728,scored,policies/dishwasher_load_cutlery/gpt61terra-s3/policy.py
392
+ dishwasher_load_plates,extended,gpt61terra-s1,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,14,44,90029,0.75,90559,10237824,28764,scored,policies/dishwasher_load_plates/gpt61terra-s1/policy.py
393
+ dishwasher_load_plates,extended,gpt61terra-s2,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,7,71,83599,0.84,125740,11471616,28697,scored,policies/dishwasher_load_plates/gpt61terra-s2/policy.py
394
+ dishwasher_load_plates,extended,gpt61terra-s3,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,7,56,99279,0.93,166678,12973696,23863,scored,policies/dishwasher_load_plates/gpt61terra-s3/policy.py
395
+ flip_cup,extended,gpt61terra-s1,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,4,61,100613,0.71,102528,8874112,32701,scored,policies/flip_cup/gpt61terra-s1/policy.py
396
+ flip_cup,extended,gpt61terra-s2,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,2,65,100522,0.67,95250,9125504,23874,scored,policies/flip_cup/gpt61terra-s2/policy.py
397
+ flip_cup,extended,gpt61terra-s3,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,11,5,62,99768,0.74,208184,8265472,22878,scored,policies/flip_cup/gpt61terra-s3/policy.py
398
+ flip_cutlery,extended,gpt61terra-s1,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,3,3,80,95340,0.78,113036,10792448,24865,scored,policies/flip_cutlery/gpt61terra-s1/policy.py
399
+ flip_cutlery,extended,gpt61terra-s2,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,2,90,99755,0.64,96904,8498432,23176,scored,policies/flip_cutlery/gpt61terra-s2/policy.py
400
+ flip_cutlery,extended,gpt61terra-s3,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,1,37,99228,0.63,158757,6823680,25373,scored,policies/flip_cutlery/gpt61terra-s3/policy.py
401
+ put_cups,extended,gpt61terra-s1,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,4,49,96760,0.54,86884,6914176,21985,scored,policies/put_cups/gpt61terra-s1/policy.py
402
+ put_cups,extended,gpt61terra-s2,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,15,62,98660,0.64,96275,7722752,30687,scored,policies/put_cups/gpt61terra-s2/policy.py
403
+ put_cups,extended,gpt61terra-s3,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,2,40,12940,0.25,61814,2774528,10661,scored,policies/put_cups/gpt61terra-s3/policy.py
404
+ reach_target_single,extended,gpt61terra-s1,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,30,6,29,100406,0.33,61329,3848960,14967,scored,policies/reach_target_single/gpt61terra-s1/policy.py
405
+ reach_target_single,extended,gpt61terra-s2,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,66,6,34,101000,0.3,93833,2734592,14649,scored,policies/reach_target_single/gpt61terra-s2/policy.py
406
+ reach_target_single,extended,gpt61terra-s3,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,78,4,91,100112,0.4,94014,4663552,15357,scored,policies/reach_target_single/gpt61terra-s3/policy.py
407
+ sandwich_remove,extended,gpt61terra-s1,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,2,43,94222,0.76,117504,10146432,27209,scored,policies/sandwich_remove/gpt61terra-s1/policy.py
408
+ sandwich_remove,extended,gpt61terra-s2,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,2,33,57777,0.54,123946,6094080,22380,scored,policies/sandwich_remove/gpt61terra-s2/policy.py
409
+ sandwich_remove,extended,gpt61terra-s3,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,2,44,56010,0.48,68793,6278784,19039,scored,policies/sandwich_remove/gpt61terra-s3/policy.py
410
+ saucepan_to_hob,extended,gpt61terra-s1,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,1,41,100545,0.81,124926,11319296,24054,scored,policies/saucepan_to_hob/gpt61terra-s1/policy.py
411
+ saucepan_to_hob,extended,gpt61terra-s2,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,2,52,100934,0.77,107157,10763648,25228,scored,policies/saucepan_to_hob/gpt61terra-s2/policy.py
412
+ saucepan_to_hob,extended,gpt61terra-s3,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,1,40,38951,0.32,62405,3927552,11898,scored,policies/saucepan_to_hob/gpt61terra-s3/policy.py
413
+ stack_blocks,extended,gpt61terra-s1,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,3,76,99440,0.86,115164,11490944,33586,scored,policies/stack_blocks/gpt61terra-s1/policy.py
414
+ stack_blocks,extended,gpt61terra-s2,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,5,108,88720,1.16,144596,17021056,32016,scored,policies/stack_blocks/gpt61terra-s2/policy.py
415
+ stack_blocks,extended,gpt61terra-s3,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,6,136,98620,1.01,170707,13534592,31994,scored,policies/stack_blocks/gpt61terra-s3/policy.py
416
+ wall_cupboard_close,extended,gpt61terra-s1,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,2,48,101000,0.41,79880,4750464,17830,scored,policies/wall_cupboard_close/gpt61terra-s1/policy.py
417
+ wall_cupboard_close,extended,gpt61terra-s2,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,100,1,17,53820,0.22,78406,1948416,8433,scored,policies/wall_cupboard_close/gpt61terra-s2/policy.py
418
+ wall_cupboard_close,extended,gpt61terra-s3,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,1,41,99593,0.36,69664,4271232,16132,scored,policies/wall_cupboard_close/gpt61terra-s3/policy.py
419
+ wall_cupboard_open,extended,gpt61terra-s1,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,2,50,99699,0.56,171769,5635200,21473,scored,policies/wall_cupboard_open/gpt61terra-s1/policy.py
420
+ wall_cupboard_open,extended,gpt61terra-s2,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,4,59,99078,0.58,85954,7069568,28030,scored,policies/wall_cupboard_open/gpt61terra-s2/policy.py
421
+ wall_cupboard_open,extended,gpt61terra-s3,gpt61terra,GPT-6.1 Terra,Codex CLI,codex-cli 0.164.0,high,default,0,6,28,98905,0.86,103286,12311168,28846,scored,policies/wall_cupboard_open/gpt61terra-s3/policy.py
policies/stack_blocks/deepseek1017-s2/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ seed,success,length,termination,fell
2
+ 620000,0,8750,timeout,0
3
+ 620001,0,8750,timeout,0
4
+ 620002,0,8750,timeout,0
5
+ 620003,0,8750,timeout,0
6
+ 620004,0,8750,timeout,0
7
+ 620005,0,8750,timeout,0
8
+ 620006,0,8750,timeout,0
9
+ 620007,0,8750,timeout,0
10
+ 620008,0,8750,timeout,0
11
+ 620009,0,8750,timeout,0
12
+ 620010,0,8750,timeout,0
13
+ 620011,0,8750,timeout,0
14
+ 620012,0,8750,timeout,0
15
+ 620013,0,8750,timeout,0
16
+ 620014,0,8750,timeout,0
17
+ 620015,0,8750,timeout,0
18
+ 620016,0,8750,timeout,0
19
+ 620017,0,8750,timeout,0
20
+ 620018,0,8750,timeout,0
21
+ 620019,0,8750,timeout,0
22
+ 620020,0,8750,timeout,0
23
+ 620021,0,8750,timeout,0
24
+ 620022,0,8750,timeout,0
25
+ 620023,0,8750,timeout,0
26
+ 620024,0,8750,timeout,0
27
+ 620025,0,8750,timeout,0
28
+ 620026,0,8750,timeout,0
29
+ 620027,0,8750,timeout,0
30
+ 620028,0,8750,timeout,0
31
+ 620029,0,8750,timeout,0
32
+ 620030,0,8750,timeout,0
33
+ 620031,0,8750,timeout,0
34
+ 620032,0,8750,timeout,0
35
+ 620033,0,8750,timeout,0
36
+ 620034,0,8750,timeout,0
37
+ 620035,0,8750,timeout,0
38
+ 620036,0,8750,timeout,0
39
+ 620037,0,8750,timeout,0
40
+ 620038,0,8750,timeout,0
41
+ 620039,0,8750,timeout,0
42
+ 620040,0,8750,timeout,0
43
+ 620041,0,8750,timeout,0
44
+ 620042,0,8750,timeout,0
45
+ 620043,0,8750,timeout,0
46
+ 620044,0,8750,timeout,0
47
+ 620045,0,8750,timeout,0
48
+ 620046,0,8750,timeout,0
49
+ 620047,0,8750,timeout,0
50
+ 620048,0,8750,timeout,0
51
+ 620049,0,8750,timeout,0
52
+ 620050,0,8750,timeout,0
53
+ 620051,0,8750,timeout,0
54
+ 620052,0,8750,timeout,0
55
+ 620053,0,8750,timeout,0
56
+ 620054,0,8750,timeout,0
57
+ 620055,0,8750,timeout,0
58
+ 620056,0,8750,timeout,0
59
+ 620057,0,8750,timeout,0
60
+ 620058,0,8750,timeout,0
61
+ 620059,0,8750,timeout,0
62
+ 620060,0,8750,timeout,0
63
+ 620061,0,8750,timeout,0
64
+ 620062,0,8750,timeout,0
65
+ 620063,0,8750,timeout,0
66
+ 620064,0,8750,timeout,0
67
+ 620065,0,8750,timeout,0
68
+ 620066,0,8750,timeout,0
69
+ 620067,0,8750,timeout,0
70
+ 620068,0,8750,timeout,0
71
+ 620069,0,8750,timeout,0
72
+ 620070,0,8750,timeout,0
73
+ 620071,0,8750,timeout,0
74
+ 620072,0,8750,timeout,0
75
+ 620073,0,8750,timeout,0
76
+ 620074,0,8750,timeout,0
77
+ 620075,0,8750,timeout,0
78
+ 620076,0,8750,timeout,0
79
+ 620077,0,8750,timeout,0
80
+ 620078,0,8750,timeout,0
81
+ 620079,0,8750,timeout,0
82
+ 620080,0,8750,timeout,0
83
+ 620081,0,8750,timeout,0
84
+ 620082,0,8750,timeout,0
85
+ 620083,0,8750,timeout,0
86
+ 620084,0,8750,timeout,0
87
+ 620085,0,8750,timeout,0
88
+ 620086,0,8750,timeout,0
89
+ 620087,0,8750,timeout,0
90
+ 620088,0,8750,timeout,0
91
+ 620089,0,8750,timeout,0
92
+ 620090,0,8750,timeout,0
93
+ 620091,0,8750,timeout,0
94
+ 620092,0,8750,timeout,0
95
+ 620093,0,8750,timeout,0
96
+ 620094,0,8750,timeout,0
97
+ 620095,0,8750,timeout,0
98
+ 620096,0,8750,timeout,0
99
+ 620097,0,8750,timeout,0
100
+ 620098,0,8750,timeout,0
101
+ 620099,0,8750,timeout,0
policies/stack_blocks/deepseek1017-s2/policy.py ADDED
@@ -0,0 +1,303 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Control policy for the G1 ``stack_blocks`` task.
2
+
3
+ The robot starts at the source table with red blocks right next to its open
4
+ grippers. The destination is another table carrying a green pad which may
5
+ appear anywhere in the room, so the policy grabs the blocks first, scans for
6
+ the green pad with the head camera and then carries the blocks to it.
7
+
8
+ Only the robot's own cameras and proprioception are used. All logic is
9
+ deterministic (no wall clock, no unseeded randomness).
10
+ """
11
+
12
+ import cv2
13
+ import numpy as np
14
+
15
+
16
+ def _wrap(a):
17
+ return (a + np.pi) % (2 * np.pi) - np.pi
18
+
19
+
20
+ class Policy:
21
+ # ---------------------------------------------------------------- helpers
22
+ @staticmethod
23
+ def _green_blob(im):
24
+ """Largest green blob in an 84x84 RGB image -> (area, cx, cy)."""
25
+ r = im[:, :, 0].astype(np.int16)
26
+ g = im[:, :, 1].astype(np.int16)
27
+ b = im[:, :, 2].astype(np.int16)
28
+ m = ((g > r + 18) & (g > b + 18) & (g > 52)).astype(np.uint8) * 255
29
+ if m.sum() == 0:
30
+ return 0, 42.0, 42.0
31
+ try:
32
+ n, lab, stats, cents = cv2.connectedComponentsWithStats(m, 8)
33
+ except Exception:
34
+ return 0, 42.0, 42.0
35
+ best, bx, by = 0, 42.0, 42.0
36
+ for i in range(1, n):
37
+ a = int(stats[i, 4])
38
+ if a > best:
39
+ best = a
40
+ bx, by = float(cents[i, 0]), float(cents[i, 1])
41
+ return best, bx, by
42
+
43
+ def _carry(self, raw, lift=0.32, elbow=0.14):
44
+ raw[5] = lift
45
+ raw[6] = 0.0
46
+ raw[7] = elbow
47
+ raw[8] = 0.0
48
+ raw[9] = 0.0
49
+ raw[10] = 0.0
50
+ raw[11] = 0.0
51
+ raw[12] = lift
52
+ raw[13] = 0.0
53
+ raw[14] = elbow
54
+ raw[15] = 0.0
55
+ raw[16] = 0.0
56
+ raw[17] = 0.0
57
+ raw[18] = 0.0
58
+ raw[19] = 1.0
59
+ raw[20] = 1.0
60
+ return raw
61
+
62
+ def _normal(self, raw):
63
+ raw[2] = 0.74
64
+ raw[4] = 0.0
65
+ return raw
66
+
67
+ # ------------------------------------------------------------------ reset
68
+ def reset(self, obs, tools):
69
+ ld = np.asarray(obs["low_dim_obs"])
70
+ self.sx = float(ld[21])
71
+ self.sy = float(ld[22])
72
+ self.syaw = float(ld[24])
73
+ self.t = 0
74
+ self.state = "grab"
75
+ self.scan_k = 0
76
+ self.scan_t = 0
77
+ self.scan_phase = 0
78
+ self.leg = 0
79
+ self.legs = [
80
+ (self.sx - 1.0, self.sy - 2.6),
81
+ (self.sx + 6.2, self.sy - 2.6),
82
+ (self.sx + 6.2, self.sy - 5.0),
83
+ (self.sx + 1.0, self.sy - 5.0),
84
+ (self.sx + 1.0, self.sy - 6.8),
85
+ (self.sx + 6.5, self.sy - 6.8),
86
+ (self.sx + 6.5, self.sy + 2.6),
87
+ (self.sx + 1.0, self.sy + 2.6),
88
+ (self.sx + 1.0, self.sy + 5.0),
89
+ (self.sx - 2.0, self.sy + 5.0),
90
+ (self.sx - 2.0, self.sy + 2.0),
91
+ (self.sx - 4.0, self.sy + 2.0),
92
+ (self.sx - 4.0, self.sy - 2.0),
93
+ (self.sx - 6.0, self.sy - 2.0),
94
+ ]
95
+ self.green_seen = 0
96
+ self.last_blob = (0, 42.0, 42.0)
97
+ self.place_t = 0
98
+ self.approach_t = 0
99
+ self.last_pos = (self.sx, self.sy)
100
+ self.approach_stall = 0
101
+ self.wrist_seen = 0
102
+
103
+ # -------------------------------------------------------------------- act
104
+ def act(self, obs, tools):
105
+ ld = np.asarray(obs["low_dim_obs"])
106
+ x = float(ld[21])
107
+ y = float(ld[22])
108
+ z = float(ld[23])
109
+ yaw = float(ld[24])
110
+ raw = np.asarray(tools.hold_action(), dtype=np.float32)
111
+ t = self.t
112
+ self.t += 1
113
+
114
+ if z < 0.45: # fallen: freeze
115
+ return raw
116
+
117
+ # ------------------------------------------------ 1. grasp the blocks
118
+ if self.state == "grab":
119
+ raw = self._carry(raw, lift=0.30, elbow=0.12)
120
+ if t >= 50:
121
+ self.state = "retreat"
122
+ return raw
123
+
124
+ # ------------------------------------------------ 2. back away
125
+ if self.state == "retreat":
126
+ raw = self._carry(raw, lift=0.30, elbow=0.12)
127
+ raw[0] = -0.45
128
+ if t >= 165:
129
+ self.state = "scan0"
130
+ self.scan_k = 0
131
+ self.scan_phase = 0
132
+ self.scan_t = 0
133
+ return raw
134
+
135
+ # ------------------------------------------------ 3. initial tall scan
136
+ if self.state == "scan0":
137
+ target = self.syaw + self.scan_k * np.pi / 4.0
138
+ err = _wrap(target - yaw)
139
+ if self.scan_phase == 0:
140
+ # rotate at normal height
141
+ raw = self._carry(raw)
142
+ self._normal(raw)
143
+ if abs(err) > 0.06 and self.scan_t < 70:
144
+ raw[3] = float(np.clip(2.4 * err, -0.65, 0.65))
145
+ self.scan_t += 1
146
+ return raw
147
+ self.scan_phase = 1
148
+ self.scan_t = 0
149
+ if self.scan_phase == 1:
150
+ # settle tall, lean back and look around
151
+ raw = self._carry(raw)
152
+ raw[2] = 0.92
153
+ raw[4] = -0.2
154
+ if self.scan_t < 30:
155
+ self.scan_t += 1
156
+ return raw
157
+ try:
158
+ im = tools.image("head")
159
+ area, cx, cy = self._green_blob(im)
160
+ except Exception:
161
+ area, cx, cy = 0, 42.0, 42.0
162
+ if area >= 5:
163
+ self.state = "approach"
164
+ self.last_blob = (area, cx, cy)
165
+ self.approach_t = 0
166
+ return raw
167
+ self.scan_phase = 2
168
+ self.scan_t = 0
169
+ if self.scan_phase == 2:
170
+ # back to normal before next rotation
171
+ raw = self._carry(raw)
172
+ self._normal(raw)
173
+ self.scan_t += 1
174
+ if self.scan_t >= 12:
175
+ self.scan_k += 1
176
+ self.scan_phase = 0
177
+ self.scan_t = 0
178
+ if self.scan_k >= 8:
179
+ self.state = "search"
180
+ self.leg = 0
181
+ return raw
182
+
183
+ # ------------------------------------------------ 4. search the room
184
+ if self.state == "search":
185
+ try:
186
+ im = tools.image("head")
187
+ area, cx, cy = self._green_blob(im)
188
+ except Exception:
189
+ area, cx, cy = 0, 42.0, 42.0
190
+ if area < 9 and self.t % 4 == 0:
191
+ # the wrist cameras can spot a near pad the head camera misses
192
+ try:
193
+ wl = tools.image("left_wrist")
194
+ ar1, _, _ = self._green_blob(wl)
195
+ wr = tools.image("right_wrist")
196
+ ar2, _, _ = self._green_blob(wr)
197
+ except Exception:
198
+ ar1 = ar2 = 0
199
+ if max(ar1, ar2) >= 9:
200
+ self.wrist_seen += 1
201
+ else:
202
+ self.wrist_seen = 0
203
+ if self.wrist_seen >= 2:
204
+ self.state = "approach"
205
+ self.last_blob = (max(ar1, ar2), 42.0, 60.0)
206
+ self.approach_t = 0
207
+ return raw
208
+ if area >= 8:
209
+ self.green_seen += 1
210
+ else:
211
+ self.green_seen = 0
212
+ if self.green_seen >= 2:
213
+ self.state = "approach"
214
+ self.last_blob = (area, cx, cy)
215
+ self.approach_t = 0
216
+ return raw
217
+
218
+ if self.leg >= len(self.legs):
219
+ self.state = "idle"
220
+ return raw
221
+ tx, ty = self.legs[self.leg]
222
+ dx, dy = tx - x, ty - y
223
+ dist = float(np.hypot(dx, dy))
224
+ if dist < 0.45:
225
+ self.leg += 1
226
+ return raw
227
+ target = float(np.arctan2(dy, dx))
228
+ err = _wrap(target - yaw)
229
+ raw = self._carry(raw)
230
+ raw[0] = 0.55 if abs(err) < 0.7 else 0.05
231
+ raw[3] = float(np.clip(2.2 * err, -0.55, 0.55))
232
+ return raw
233
+
234
+ # ------------------------------------------------ 5. approach the pad
235
+ if self.state == "approach":
236
+ self.approach_t += 1
237
+ try:
238
+ im = tools.image("head")
239
+ area, cx, cy = self._green_blob(im)
240
+ except Exception:
241
+ area, cx, cy = 0, 42.0, 42.0
242
+ if area >= 5:
243
+ self.last_blob = (area, cx, cy)
244
+ else:
245
+ area, cx, cy = self.last_blob
246
+ bearing = -(cx - 42.0) / 42.0 * 0.55
247
+ raw = self._carry(raw)
248
+ raw[3] = float(np.clip(1.8 * bearing, -0.45, 0.45))
249
+ if area < 40:
250
+ raw[0] = 0.32
251
+ elif area < 120:
252
+ raw[0] = 0.18
253
+ else:
254
+ raw[0] = 0.0
255
+ # stalled against the table -> place now
256
+ moved = ((x - self.last_pos[0]) ** 2 + (y - self.last_pos[1]) ** 2) ** 0.5
257
+ if self.approach_t % 20 == 0:
258
+ if moved < 0.05:
259
+ self.approach_stall += 1
260
+ else:
261
+ self.approach_stall = 0
262
+ self.last_pos = (x, y)
263
+ if self.approach_t > 25 and (area >= 110 or (area >= 60 and cy > 46)):
264
+ self.state = "place"
265
+ self.place_t = 0
266
+ if self.approach_t > 300 or self.approach_stall >= 2:
267
+ self.state = "place"
268
+ self.place_t = 0
269
+ return raw
270
+
271
+ # ------------------------------------------------ 6. place the blocks
272
+ if self.state == "place":
273
+ self.place_t += 1
274
+ raw[0] = 0.0
275
+ raw[2] = 0.66
276
+ raw[4] = 0.55
277
+ raw[5] = 0.95
278
+ raw[6] = 0.0
279
+ raw[7] = -0.10
280
+ raw[8] = 0.0
281
+ raw[9] = 0.0
282
+ raw[10] = 0.0
283
+ raw[11] = 0.0
284
+ raw[12] = 0.95
285
+ raw[13] = 0.0
286
+ raw[14] = -0.10
287
+ raw[15] = 0.0
288
+ raw[16] = 0.0
289
+ raw[17] = 0.0
290
+ raw[18] = 0.0
291
+ if self.place_t < 40:
292
+ raw[19] = 1.0
293
+ raw[20] = 1.0
294
+ else:
295
+ raw[19] = 0.0
296
+ raw[20] = 0.0
297
+ if self.place_t > 80:
298
+ self.state = "idle"
299
+ return raw
300
+
301
+ # ------------------------------------------------------------- idle
302
+ raw = self._carry(raw, lift=0.0, elbow=0.0)
303
+ return raw
policies/stack_blocks/deepseek1017-s3/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ seed,success,length,termination,fell
2
+ 620000,0,8750,timeout,0
3
+ 620001,0,8750,timeout,0
4
+ 620002,0,8750,timeout,0
5
+ 620003,0,8750,timeout,0
6
+ 620004,0,8750,timeout,0
7
+ 620005,0,8750,timeout,0
8
+ 620006,0,8750,timeout,0
9
+ 620007,0,8750,timeout,0
10
+ 620008,0,8750,timeout,0
11
+ 620009,0,8750,timeout,0
12
+ 620010,0,8750,timeout,0
13
+ 620011,0,8750,timeout,0
14
+ 620012,0,8750,timeout,0
15
+ 620013,0,8750,timeout,0
16
+ 620014,0,8750,timeout,0
17
+ 620015,0,8750,timeout,0
18
+ 620016,0,8750,timeout,0
19
+ 620017,0,8750,timeout,0
20
+ 620018,0,8750,timeout,0
21
+ 620019,0,579,terminated,0
22
+ 620020,0,8750,timeout,0
23
+ 620021,0,8750,timeout,0
24
+ 620022,0,8750,timeout,0
25
+ 620023,0,8750,timeout,0
26
+ 620024,0,8750,timeout,0
27
+ 620025,0,8750,timeout,0
28
+ 620026,0,8750,timeout,0
29
+ 620027,0,8750,timeout,0
30
+ 620028,0,8750,timeout,0
31
+ 620029,0,8750,timeout,0
32
+ 620030,0,8750,timeout,0
33
+ 620031,0,634,terminated,0
34
+ 620032,0,8750,timeout,0
35
+ 620033,0,8750,timeout,0
36
+ 620034,0,8750,timeout,0
37
+ 620035,0,8750,timeout,0
38
+ 620036,0,8750,timeout,0
39
+ 620037,0,8750,timeout,0
40
+ 620038,0,8750,timeout,0
41
+ 620039,0,8750,timeout,0
42
+ 620040,0,8750,timeout,0
43
+ 620041,0,8750,timeout,0
44
+ 620042,0,8750,timeout,0
45
+ 620043,0,8750,timeout,0
46
+ 620044,0,8750,timeout,0
47
+ 620045,0,8750,timeout,0
48
+ 620046,0,8750,timeout,0
49
+ 620047,0,8750,timeout,0
50
+ 620048,0,8750,timeout,0
51
+ 620049,0,8750,timeout,0
52
+ 620050,0,8750,timeout,0
53
+ 620051,0,8750,timeout,0
54
+ 620052,0,8750,timeout,0
55
+ 620053,0,8750,timeout,0
56
+ 620054,0,8750,timeout,0
57
+ 620055,0,8750,timeout,0
58
+ 620056,0,8750,timeout,0
59
+ 620057,0,8750,timeout,0
60
+ 620058,0,8750,timeout,0
61
+ 620059,0,8750,timeout,0
62
+ 620060,0,8750,timeout,0
63
+ 620061,0,8750,timeout,0
64
+ 620062,0,8750,timeout,0
65
+ 620063,0,8750,timeout,0
66
+ 620064,0,8750,timeout,0
67
+ 620065,0,8750,timeout,0
68
+ 620066,0,8750,timeout,0
69
+ 620067,0,8750,timeout,0
70
+ 620068,0,8750,timeout,0
71
+ 620069,0,8750,timeout,0
72
+ 620070,0,8750,timeout,0
73
+ 620071,0,8750,timeout,0
74
+ 620072,0,8750,timeout,0
75
+ 620073,0,8750,timeout,0
76
+ 620074,0,8750,timeout,0
77
+ 620075,0,8750,timeout,0
78
+ 620076,0,8750,timeout,0
79
+ 620077,0,8750,timeout,0
80
+ 620078,0,8750,timeout,0
81
+ 620079,0,8750,timeout,0
82
+ 620080,0,8750,timeout,0
83
+ 620081,0,8750,timeout,0
84
+ 620082,0,8750,timeout,0
85
+ 620083,0,8750,timeout,0
86
+ 620084,0,8750,timeout,0
87
+ 620085,0,8750,timeout,0
88
+ 620086,0,8750,timeout,0
89
+ 620087,0,8750,timeout,0
90
+ 620088,0,8750,timeout,0
91
+ 620089,0,8750,timeout,0
92
+ 620090,0,8750,timeout,0
93
+ 620091,0,8750,timeout,0
94
+ 620092,0,8750,timeout,0
95
+ 620093,0,8750,timeout,0
96
+ 620094,0,8750,timeout,0
97
+ 620095,0,8750,timeout,0
98
+ 620096,0,8750,timeout,0
99
+ 620097,0,8750,timeout,0
100
+ 620098,0,8750,timeout,0
101
+ 620099,0,8750,timeout,0
policies/stack_blocks/deepseek1017-s3/policy.py ADDED
@@ -0,0 +1,236 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Hand-written control policy for the G1 "stack blocks" task.
2
+
3
+ Pipeline (all control is deterministic, fixed thresholds tuned on the dev seeds):
4
+
5
+ 1. grasp -- the ready pose puts both grippers just behind two of the three
6
+ blocks. Lean the torso slightly, strafe right, creep forward while the
7
+ block in the right wrist camera is tracked, then squat, close both
8
+ grippers and lift the arms.
9
+ 2. carry -- walk around the block table (north side) to the east side of the
10
+ green pad table using pelvis odometry.
11
+ 3. place -- face the pad, push up to the table edge, lean forward so the
12
+ hands reach over the pad, and release the blocks.
13
+
14
+ Only the robot's own cameras and proprioception are used.
15
+ """
16
+
17
+ import cv2
18
+ import numpy as np
19
+
20
+
21
+ # ----------------------------------------------------------------------------
22
+ # small image helpers
23
+ # ----------------------------------------------------------------------------
24
+ def _red_blob(img):
25
+ """Largest red blob of an RGB image -> (cx, cy, area) or None."""
26
+ r = img[..., 0].astype(np.int16)
27
+ g = img[..., 1].astype(np.int16)
28
+ b = img[..., 2].astype(np.int16)
29
+ m = ((r > 80) & (r > g + 40) & (r > b + 40)).astype(np.uint8)
30
+ n, _lab, stats, cent = cv2.connectedComponentsWithStats(m, 8)
31
+ best = None
32
+ for i in range(1, n):
33
+ a = int(stats[i, cv2.CC_STAT_AREA])
34
+ if a >= 10 and (best is None or a > best[2]):
35
+ best = (float(cent[i][0]), float(cent[i][1]), a)
36
+ return best
37
+
38
+
39
+ def _green_blob(img):
40
+ """Largest green (target pad) blob of an RGB image -> (cx, cy, area)."""
41
+ r = img[..., 0].astype(np.int16)
42
+ g = img[..., 1].astype(np.int16)
43
+ b = img[..., 2].astype(np.int16)
44
+ m = ((g > r + 25) & (g > b + 25) & (g > 100)).astype(np.uint8)
45
+ if int(m.sum()) == 0:
46
+ return None
47
+ ys, xs = np.where(m)
48
+ return (float(xs.mean()), float(ys.mean()), int(m.sum()))
49
+
50
+
51
+ def _wrap(a):
52
+ return (a + np.pi) % (2.0 * np.pi) - np.pi
53
+
54
+
55
+ # waypoints: around the north side of the block table, then south along its
56
+ # east side to the green pad table.
57
+ WAYPOINTS = ((0.30, 1.15), (1.90, 1.05), (2.15, 0.0))
58
+
59
+ # phase lengths (control steps)
60
+ P_PITCH = 30
61
+ P_STRAFE = 20
62
+ P_FWD = 175
63
+ P_SQUAT = 35
64
+ P_CLOSE = 30
65
+ P_LIFT = 30
66
+
67
+
68
+ class Policy:
69
+ """Deterministic scripted pick / carry / place policy."""
70
+
71
+ def reset(self, obs, tools):
72
+ self.t = 0
73
+ self.hold = np.asarray(tools.hold_action(), dtype=np.float64)
74
+ self.phase = 0
75
+ self.phase_t = 0
76
+ self.done = False
77
+ self.wp = 0
78
+ self.nav_t = 0
79
+ self.nav_last = None
80
+ self.nav_stuck = 0
81
+ self.pitch = 0.0
82
+
83
+ # ------------------------------------------------------------------
84
+ def _pose(self, obs):
85
+ ld = obs["low_dim_obs"]
86
+ return (float(ld[21]), float(ld[22]), float(ld[23]), float(ld[24]))
87
+
88
+ def act(self, obs, tools):
89
+ self.t += 1
90
+ self.phase_t += 1
91
+ raw = self.hold.copy()
92
+
93
+ # ---------------------------------------------------------- grasp
94
+ if self.phase <= 3:
95
+ raw[19] = 0.6
96
+ raw[20] = 0.6
97
+ elif self.phase <= 9:
98
+ raw[19] = 1.0
99
+ raw[20] = 1.0
100
+
101
+ if self.phase == 0:
102
+ raw[4] = 0.10
103
+ if self.phase_t >= P_PITCH:
104
+ self._next()
105
+ elif self.phase == 1:
106
+ raw[4] = 0.10
107
+ raw[1] = -0.15
108
+ if self.phase_t >= P_STRAFE:
109
+ self._next()
110
+ elif self.phase == 2:
111
+ raw[4] = 0.10
112
+ cur = _red_blob(tools.image("right_wrist"))
113
+ if cur is None:
114
+ raw[0] = 0.0
115
+ else:
116
+ cx, cy, _area = cur
117
+ raw[0] = 0.0 if cy > 50.0 else 0.10
118
+ vy = float(np.clip(-0.006 * (cx - 43.0), -0.15, 0.15))
119
+ raw[1] = 0.0 if abs(vy) < 0.06 else vy
120
+ if cy > 50.0 and abs(cx - 43.0) < 8.0:
121
+ self._next()
122
+ if self.phase == 2 and self.phase_t >= P_FWD:
123
+ self._next()
124
+ elif self.phase == 3:
125
+ raw[2] = 0.66
126
+ raw[4] = 0.10
127
+ if self.phase_t >= P_SQUAT:
128
+ self._next()
129
+ elif self.phase == 4:
130
+ raw[2] = 0.66
131
+ raw[4] = 0.10
132
+ if self.phase_t >= P_CLOSE:
133
+ self._next()
134
+ elif self.phase == 5:
135
+ raw[2] = 0.66
136
+ raw[4] = 0.10
137
+ raw[5] = -0.30
138
+ raw[12] = -0.30
139
+ if self.phase_t >= P_LIFT:
140
+ self._next()
141
+ # ---------------------------------------------------------- carry
142
+ elif self.phase == 6:
143
+ raw[5] = -0.30
144
+ raw[12] = -0.30
145
+ raw[4] = 0.10
146
+ # stand up very gently from the grasp squat so the held blocks do
147
+ # not slip out of the grippers
148
+ raw[2] = 0.66 + 0.08 * min(1.0, self.phase_t / 250.0)
149
+ if self.phase_t < 250:
150
+ # stand up in place first; do not disturb the grip while the
151
+ # body rises
152
+ return raw
153
+ if self.wp >= len(WAYPOINTS):
154
+ self._next()
155
+ else:
156
+ p = self._pose(obs)
157
+ tx, ty = WAYPOINTS[self.wp]
158
+ dx, dy = tx - p[0], ty - p[1]
159
+ d = float(np.hypot(dx, dy))
160
+ if self.nav_last is not None and abs(p[3]) < 50.0:
161
+ step_moved = float(np.hypot(p[0] - self.nav_last[0],
162
+ p[1] - self.nav_last[1]))
163
+ if step_moved < 0.004 and self.nav_t > 90:
164
+ self.nav_stuck += 1
165
+ else:
166
+ self.nav_stuck = 0
167
+ self.nav_last = p
168
+ self.nav_t += 1
169
+ if d < 0.16 or self.nav_stuck > 45 or self.nav_t > 420:
170
+ self.wp += 1
171
+ self.nav_t = 0
172
+ self.nav_last = None
173
+ self.nav_stuck = 0
174
+ else:
175
+ des = float(np.arctan2(dy, dx))
176
+ err = _wrap(des - p[3])
177
+ raw[3] = float(np.clip(2.2 * err, -0.8, 0.8))
178
+ raw[0] = float(
179
+ 0.38 * max(0.0, min(1.0, (0.9 - abs(err)) / 0.6))
180
+ * max(0.3, min(1.0, d / 0.4))
181
+ )
182
+ # ---------------------------------------------------------- place
183
+ elif self.phase == 7:
184
+ raw[5] = -0.30
185
+ raw[12] = -0.30
186
+ raw[4] = 0.05
187
+ p = self._pose(obs)
188
+ err = _wrap(np.pi - p[3])
189
+ if abs(err) < 0.04 or self.phase_t > 260:
190
+ self._next()
191
+ else:
192
+ raw[3] = float(np.clip(2.5 * err, -0.9, 0.9))
193
+ elif self.phase == 8:
194
+ raw[5] = -0.30
195
+ raw[12] = -0.30
196
+ raw[4] = 0.10
197
+ raw[0] = 0.15
198
+ raw[3] = float(np.clip(2.5 * _wrap(np.pi - self._pose(obs)[3]), -0.5, 0.5))
199
+
200
+ if self.phase_t >= 70:
201
+ self._next()
202
+ elif self.phase == 9:
203
+ # reach down-forward over the table towards the pad
204
+ raw[2] = 0.72
205
+ raw[4] = 0.35
206
+ raw[5] = 0.35
207
+ raw[12] = 0.35
208
+ if self.phase_t >= 30:
209
+ self._next()
210
+ elif self.phase == 10:
211
+ raw[2] = 0.72
212
+ raw[4] = 0.40
213
+ raw[5] = 0.45
214
+ raw[12] = 0.45
215
+ raw[19] = 0.0
216
+ raw[20] = 0.0
217
+ if self.phase_t >= 40:
218
+ self._next()
219
+ elif self.phase == 11:
220
+ # settle and lift the empty grippers clear of the pad
221
+ raw[2] = 0.74
222
+ raw[4] = 0.20
223
+ raw[5] = 0.10
224
+ raw[12] = 0.10
225
+ raw[19] = 0.0
226
+ raw[20] = 0.0
227
+ if self.phase_t >= 30:
228
+ self._next()
229
+ else:
230
+ raw = self.hold.copy()
231
+ raw[4] = 0.1
232
+ return raw
233
+
234
+ def _next(self):
235
+ self.phase += 1
236
+ self.phase_t = 0
policies/stack_blocks/gpt61sol-s1/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ seed,success,length,termination,fell
2
+ 620000,0,8750,timeout,0
3
+ 620001,0,8750,timeout,0
4
+ 620002,0,4495,terminated,0
5
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6
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7
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8
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9
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10
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11
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12
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13
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14
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15
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16
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17
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18
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19
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20
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21
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22
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23
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24
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25
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26
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27
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28
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29
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30
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31
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32
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33
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34
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35
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36
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37
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38
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39
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40
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41
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42
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43
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44
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45
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46
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47
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48
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49
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50
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51
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52
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53
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54
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55
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56
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57
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58
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59
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60
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61
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62
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63
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64
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65
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66
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67
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68
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69
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70
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71
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72
+ 620070,0,8750,timeout,0
73
+ 620071,0,7167,terminated,0
74
+ 620072,0,8750,timeout,0
75
+ 620073,0,8750,timeout,0
76
+ 620074,0,8750,timeout,0
77
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78
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79
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80
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81
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82
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83
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84
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85
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86
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87
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88
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89
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90
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91
+ 620089,0,8750,timeout,0
92
+ 620090,0,8750,timeout,0
93
+ 620091,0,3337,terminated,0
94
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95
+ 620093,0,4041,terminated,0
96
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97
+ 620095,0,8750,timeout,0
98
+ 620096,0,8750,timeout,0
99
+ 620097,0,8750,timeout,0
100
+ 620098,0,3847,terminated,0
101
+ 620099,0,8750,timeout,0
policies/stack_blocks/gpt61sol-s1/policy.py ADDED
@@ -0,0 +1,207 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Deterministic visual control for transferring and stacking three blocks.
2
+
3
+ Colour segmentation supplies image errors; body pose closes the navigation
4
+ loop. All constants and state transitions are hand-written. No demonstration,
5
+ seed identity, fitted model, or stored placement data is read at runtime.
6
+ """
7
+
8
+ import numpy as np
9
+ import cv2
10
+
11
+
12
+ def blobs(im, color='red'):
13
+ r,g,b=im[:,:,0].astype(float),im[:,:,1].astype(float),im[:,:,2].astype(float)
14
+ if color=='red': m=(r>100)&(r>1.6*g)&(r>1.6*b)
15
+ else: m=(g>95)&(g>1.35*r)&(g>1.2*b)
16
+ n,l,s,c=cv2.connectedComponentsWithStats(m.astype(np.uint8))
17
+ return [(cc,np.sqrt(ss[4]),ss) for ss,cc in zip(s[1:],c[1:]) if ss[4]>12]
18
+
19
+ class Policy:
20
+ def reset(self,obs,tools):
21
+ self.a=np.asarray(tools.hold_action(),dtype=float)
22
+ self.a[19:]=0
23
+ self.phase='open';self.start=0;self.servo=0
24
+ self.route=[(0,-1,0),(2.4,-1,0),(2.4,0,0),(2.4,0,np.pi)]
25
+ self.ri=0;self.stable=0;self.pickx=0;self.pickpitch=0;self.rightpitch=0;self.placebase=None;self.level=1;self.returning=False;self.stackoffset=None;self.held_uv=None;self.last=self.a.copy();self.limits=(np.array(tools.info['raw_low']),np.array(tools.info['raw_high']))
26
+ def transition(self,name,t):
27
+ self.phase=name;self.start=t;self.stable=0
28
+ def drive(self,p,x,y,yaw):
29
+ err=np.array([x,y])-p[:2];co=np.cos(p[3]);si=np.sin(p[3]);v=np.array([co*err[0]+si*err[1],-si*err[0]+co*err[1]])*1.2
30
+ norm=np.linalg.norm(v)
31
+ if norm>.4:v*=.4/norm
32
+ if np.linalg.norm(err)<.025:v[:]=0
33
+ elif norm<.07:v*=.07/max(norm,1e-8)
34
+ ye=(yaw-p[3]+np.pi)%(2*np.pi)-np.pi
35
+ self.a[:2]=v;self.a[3]=np.clip(ye*1.8,-.5,.5) if abs(ye)>.02 else 0
36
+ return np.linalg.norm(err)<.035 and abs(ye)<.04
37
+ def act(self,obs,tools):
38
+ t=obs['t'];p=obs['low_dim_obs'][21:25];dt=t-self.start
39
+ if self.phase in ['approach','align_left','place_left','release_left','clear_left','align_right','place_right','release_right','clear_right','arrived']:
40
+ ye=(np.pi-p[3]+np.pi)%(2*np.pi)-np.pi
41
+ self.a[3]=np.clip(ye*1.5,-.25,.25)
42
+ if self.phase=='open':
43
+ if dt>=60:self.transition('grasp',t)
44
+ elif self.phase=='grasp':
45
+ if dt%10==0:
46
+ vals=[]
47
+ for j,cam in enumerate(['left_wrist','right_wrist']):
48
+ bs=blobs(tools.image(cam));rr=min(bs,key=lambda z:abs(z[0][0]-42)) if bs else None
49
+ vals.append(rr)
50
+ if rr:
51
+ c,sz,_=rr
52
+ self.a[7+7*j]+=np.clip((42-c[0])*.0015,-.02,.02)
53
+ self.a[5+7*j]+=np.clip((c[1]-20)*.001,-.02,.02)
54
+ self.a[0]=.08 if any(rr and rr[1]<39 for rr in vals) else 0
55
+ good=all(rr and abs(rr[0][0]-42)<2 and abs(rr[0][1]-20)<2 and rr[1]>39 for rr in vals)
56
+ self.stable=self.stable+10 if good else 0
57
+ if self.stable>=80 or dt>800:
58
+ self.a[:2]=0;self.a[19]=1;self.pickx=p[0];self.pickpitch=self.a[5];self.transition('left_close',t)
59
+ elif self.phase=='left_close':
60
+ if dt>70:
61
+ self.a[5]=-.12;self.transition('right_grasp',t)
62
+ elif self.phase=='right_grasp':
63
+ if dt%10==0:
64
+ bs=blobs(tools.image('right_wrist'));rr=min(bs,key=lambda z:abs(z[0][0]-42)) if bs else None
65
+ if rr:
66
+ c,sz,_=rr
67
+ self.a[14]+=np.clip((42-c[0])*.0015,-.015,.015)
68
+ self.a[12]+=np.clip((c[1]-20)*.001,-.015,.015)
69
+ self.a[0]=.06 if sz<53 and p[0]<self.pickx+.055 else 0
70
+ good=abs(c[0]-42)<2 and abs(c[1]-20)<2 and sz>50
71
+ self.stable=self.stable+10 if good else 0
72
+ if self.stable>=70 or dt>400:
73
+ self.a[:2]=0;self.a[20]=1;self.rightpitch=self.a[12];self.transition('close',t)
74
+ elif self.phase=='close':
75
+ if dt>70:
76
+ self.a[5]=self.a[12]=-.12
77
+ self.transition('lift',t)
78
+ elif self.phase=='lift':
79
+ if dt>100:self.transition('route',t)
80
+ elif self.phase=='route':
81
+ x,y,yaw=self.route[self.ri]
82
+ if self.drive(p,x,y,yaw):
83
+ self.stable+=1
84
+ if self.stable>10:
85
+ self.ri+=1;self.stable=0
86
+ if self.ri==len(self.route):
87
+ self.a[:2]=0;self.a[3]=0
88
+ if self.returning:
89
+ self.a[12]=.20;self.a[13]=0;self.a[14]=0;self.a[20]=0;self.transition('third_approach',t)
90
+ else:self.transition('approach',t)
91
+ elif self.phase=='approach':
92
+ if dt%10==0:
93
+ bs=blobs(tools.image('head'),'green')
94
+ if bs:
95
+ c,sz,_=max(bs,key=lambda b:b[1])
96
+ self.a[0]=.1 if c[1]<42 else 0
97
+ self.a[1]=np.clip((42-c[0])*.005,-.12,.12) if abs(c[0]-42)>3 else 0
98
+ if c[1]>=42:
99
+ self.a[:2]=0;self.transition('align_left' if self.level==1 else 'align_right',t)
100
+ elif self.phase=='align_left':
101
+ if dt%10==0:
102
+ im=tools.image('head');gs=blobs(im,'green');rs=blobs(im)
103
+ if gs and rs:
104
+ gc,_,_=max(gs,key=lambda b:b[1]);rc,_,_=max([b for b in rs if b[0][0]<60 and b[1]>5] or rs,key=lambda b:b[1])
105
+ self.a[7]+=np.clip((rc[0]-gc[0])*.003,-.02,.02)
106
+ self.stable=self.stable+10 if abs(rc[0]-gc[0])<1.5 else 0
107
+ if self.stable>60 or dt>500:self.transition('place_left',t)
108
+ elif self.phase=='place_left':
109
+ self.a[5]=min(self.pickpitch,self.a[5]+.0015)
110
+ if dt%10==0 and dt>260:
111
+ im=tools.image('head');gs=blobs(im,'green');rs=blobs(im)
112
+ if gs and rs:
113
+ gc,_,st=max(gs,key=lambda b:b[1]);gc=np.array([st[0]+(st[2]-1)/2,st[1]+(st[3]-1)/2])
114
+ rc,_,_=max([b for b in rs if b[0][0]<60 and b[1]>5] or rs,key=lambda b:b[1])
115
+ self.a[7]+=np.clip((rc[0]-gc[0])*.003,-.02,.02)
116
+ dy=rc[1]-(gc[1]-4)
117
+ self.a[0]=(.065 if dy>1.5 else -.065 if dy< -1.5 else 0)
118
+ good=abs(rc[0]-gc[0])<1.5 and abs(dy)<1.5
119
+ self.stable=self.stable+10 if good else 0
120
+ if self.stable>60 or dt>350:
121
+ self.a[0]=0;self.a[19]=0;self.transition('release_left',t)
122
+ elif self.phase=='release_left':
123
+ if dt>100:
124
+ self.placebase=p[:2].copy();self.a[0]=-.08;self.transition('clear_left',t)
125
+ elif self.phase=='clear_left':
126
+ if dt>70:
127
+ self.a[0]=0;self.a[5]=-.12;self.a[7]=.5;self.a[8]=0;self.held_uv=None;self.transition('align_right',t)
128
+ elif self.phase=='align_right':
129
+ # Translate the body until the carried cube is above the base cube.
130
+ # Keeping the carrying arm in its pickup orientation avoids twisting
131
+ # the cube while bringing the two hands into the same workspace.
132
+ self.a[0]=0
133
+ if dt%10==0:
134
+ rs=[b for b in blobs(tools.image('head')) if b[1]>5]
135
+ if len(rs)>=2:
136
+ held=min(rs,key=lambda b:b[0][1])
137
+ ground=max(rs,key=lambda b:b[0][1])
138
+ dx=held[0][0]-ground[0][0]
139
+ self.a[1]=.07 if dx>1.5 else -.07 if dx< -1.5 else 0
140
+ self.stable=self.stable+10 if abs(dx)<1.5 else 0
141
+ else:self.a[1]=0
142
+ if self.stable>50 or dt>400:
143
+ self.a[:2]=0;self.upperbase=p[:2].copy()
144
+ self.transition('place_right',t)
145
+ elif self.phase=='place_right':
146
+ targetpitch=self.rightpitch-.13*self.level
147
+ self.a[12]=min(targetpitch,self.a[12]+.0015)
148
+ if dt<200:self.drive(p,self.upperbase[0]-.03,self.upperbase[1],np.pi)
149
+ elif dt%10==0:
150
+ rs=[b for b in blobs(tools.image('head')) if b[1]>5]
151
+ if len(rs)>=2:
152
+ held=min(rs,key=lambda b:b[0][1]);ground=max(rs,key=lambda b:b[0][1])
153
+ dx=held[0][0]-ground[0][0]
154
+ dy=held[0][1]-ground[0][1]+7+3.5*(self.level-1)
155
+ self.a[1]=.065 if dx>1.5 else -.065 if dx< -1.5 else 0
156
+ self.a[0]=.065 if dy>1.5 else -.065 if dy< -1.5 else 0
157
+ self.stable=self.stable+10 if abs(dx)<1.5 and abs(dy)<1.5 else 0
158
+ else:
159
+ self.a[:2]=0;self.stable+=10
160
+ if self.stable>50 or dt>450:
161
+ self.a[:2]=0;self.a[20]=0;self.transition('release_right',t)
162
+ elif self.phase=='release_right':
163
+ if dt>100:
164
+ self.a[0]=-.08;self.transition('clear_right',t)
165
+ elif self.phase=='clear_right':
166
+ if dt>70:
167
+ self.a[0]=0;self.a[12]=-.35
168
+ if self.level==2:self.transition('finished',t)
169
+ else:
170
+ self.route=[(2.4,0,np.pi),(2.4,-1,np.pi),(0,-1,0),(.08,0,0)]
171
+ self.ri=0;self.returning=True;self.transition('route',t)
172
+
173
+ elif self.phase=='third_approach':
174
+ ye=(-p[3]+np.pi)%(2*np.pi)-np.pi;self.a[3]=np.clip(ye*1.5,-.25,.25)
175
+ if dt%10==0 and dt>80:
176
+ rs=blobs(tools.image('head'))
177
+ if rs:
178
+ c,sz,_=max(rs,key=lambda b:b[1])
179
+ self.a[0]=.08 if c[1]<41 else 0
180
+ self.a[1]=.07 if c[0]<63 else -.07 if c[0]>67 else 0
181
+ if c[1]>=41 and abs(c[0]-65)<3:
182
+ self.a[:2]=0;self.transition('third_grasp',t)
183
+ elif self.phase=='third_grasp':
184
+ ye=(-p[3]+np.pi)%(2*np.pi)-np.pi
185
+ self.a[3]=np.clip(ye*1.5,-.25,.25)
186
+ if dt%10==0 and dt>70:
187
+ bs=blobs(tools.image('right_wrist'));rr=max(bs,key=lambda z:z[1]) if bs else None
188
+ if rr:
189
+ c,sz,_=rr
190
+ self.a[14]+=np.clip((42-c[0])*.0015,-.02,.02)
191
+ self.a[12]+=np.clip((c[1]-20)*.001,-.02,.02)
192
+ self.a[0]=.06 if sz<52 and abs(c[0]-42)<5 else 0
193
+ self.stable=self.stable+10 if sz>50 and abs(c[0]-42)<2 and abs(c[1]-20)<2 else 0
194
+ if self.stable>60 or dt>350:
195
+ self.a[0]=0;self.a[20]=1;self.rightpitch=self.a[12];self.transition('third_close',t)
196
+ elif self.phase=='third_close':
197
+ if dt>80:
198
+ self.a[12]=-.12;self.level=2;self.returning=False
199
+ self.route=[(0,-1,0),(2.4,-1,0),(2.4,0,0),(2.4,0,np.pi)]
200
+ self.ri=0;self.transition('route',t)
201
+ if self.phase=='finished':
202
+ self.a[:2]=0;self.a[3]=0
203
+ out=self.a.copy()
204
+ if self.phase in ['align_right','place_right','release_right','clear_right']:out[5:12]=self.last[5:12]+np.clip(out[5:12]-self.last[5:12],-.008,.008)
205
+ out=np.clip(out,self.limits[0],self.limits[1])
206
+ self.last=out.copy()
207
+ return out
policies/stack_blocks/gpt61sol-s2/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ seed,success,length,termination,fell
2
+ 620000,0,8750,timeout,0
3
+ 620001,0,8750,timeout,0
4
+ 620002,0,8750,timeout,0
5
+ 620003,0,1484,terminated,0
6
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7
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8
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9
+ 620007,0,1573,terminated,0
10
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11
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12
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13
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14
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15
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16
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17
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18
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19
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20
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21
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22
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23
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24
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25
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26
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27
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28
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29
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30
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31
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32
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33
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34
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35
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36
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37
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38
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39
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40
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41
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42
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43
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44
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45
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46
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47
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48
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49
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50
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51
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52
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53
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54
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55
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56
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57
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58
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59
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60
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61
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62
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63
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64
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65
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66
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67
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68
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69
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70
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71
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72
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73
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74
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75
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76
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77
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78
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79
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80
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81
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82
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83
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84
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85
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86
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87
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88
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89
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90
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91
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92
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93
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94
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95
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96
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97
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98
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99
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100
+ 620098,0,1546,terminated,0
101
+ 620099,0,8750,timeout,0
policies/stack_blocks/gpt61sol-s2/policy.py ADDED
@@ -0,0 +1,123 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Deterministic visual grasping, closed-loop walking, and block stacking."""
2
+ import numpy as np
3
+ import cv2
4
+
5
+ class Policy:
6
+ def reset(self, obs, tools):
7
+ self.a=np.asarray(tools.hold_action(),dtype=float)
8
+ self.target=self.a.copy();self.target[19:21]=0
9
+ self.target[5:12]=[-.6,.405,0,.4,0,0,0]
10
+ self.target[12:19]=[-.6,-.42,0,.4,0,0,0]
11
+ self.state='prepare';self.age=0;self.block=0;self.retry=0
12
+ self.route=[];self.good=0
13
+
14
+ def phase(self,s):
15
+ self.state=s;self.age=0
16
+
17
+ @staticmethod
18
+ def blob(im,green=False):
19
+ r,g,b=im[:,:,0].astype(float),im[:,:,1].astype(float),im[:,:,2].astype(float)
20
+ mask=((g>100)&(g>r*1.25)&(g>b*1.15)) if green else ((r>110)&(r>g*1.8)&(r>b*1.8))
21
+ n,lab,stats,cent=cv2.connectedComponentsWithStats(mask.astype('uint8'),8)
22
+ candidates=[i for i in range(1,n) if stats[i,4]>20]
23
+ if not candidates:return None
24
+ i=max(candidates,key=lambda i:stats[i,4])
25
+ return cent[i][0],cent[i][1],stats[i,4],stats[i,2],stats[i,3]
26
+
27
+ def nav(self,obs):
28
+ q=obs['low_dim_obs'];x,y,z,yaw=q[21:25]
29
+ gx,gy,ga=self.route[0];d=np.array([gx-x,gy-y]);er=(ga-yaw+np.pi)%(2*np.pi)-np.pi
30
+ cs,sn=np.cos(yaw),np.sin(yaw)
31
+ v=np.array([cs*d[0]+sn*d[1],-sn*d[0]+cs*d[1]])
32
+ dist=np.linalg.norm(v)
33
+ if dist<.025:v[:]=0
34
+ else:v*=min(.55,max(.08,dist*1.8))/dist
35
+ if abs(er)>.25:v*=.25
36
+ self.target[:2]=v;self.target[3]=np.clip(2*er,-.8,.8)
37
+ if (dist<.035 and abs(er)<.045) or self.age>550:
38
+ self.route.pop(0);self.age=0
39
+ if not self.route:
40
+ self.target[:2]=0;self.target[3]=0;self.phase(self.after_nav)
41
+
42
+ def act(self,obs,tools):
43
+ self.age+=1
44
+ t=self.target
45
+ if self.state=='nav':self.nav(obs)
46
+ elif self.state=='prepare':
47
+ if self.age>110:self.good=0;self.phase('source_align')
48
+ elif self.state=='source_align':
49
+ if self.age%5==0:
50
+ im=tools.image('head');r,g,b=im[:,:,0].astype(float),im[:,:,1].astype(float),im[:,:,2].astype(float)
51
+ mask=((r>110)&(r>g*1.8)&(r>b*1.8)).astype('uint8')
52
+ n,lab,st,cen=cv2.connectedComponentsWithStats(mask,8)
53
+ candidates=[i for i in range(1,n) if st[i,4]>8 and 12<cen[i,1]<45]
54
+ if candidates:
55
+ i=max(candidates,key=lambda i:cen[i,0]);cx,cy=cen[i]
56
+ ex,ey=70-cx,24-cy
57
+ vx=0.;vy=np.clip(ex*.008,-.16,.16)
58
+ if abs(ex)<1.5:vy=0
59
+ if abs(ey)<1.5:vx=0
60
+ norm=np.hypot(vx,vy)
61
+ if 0<norm<.07:vx*=.07/norm;vy*=.07/norm
62
+ t[0]=vx;t[1]=vy
63
+ self.good=self.good+5 if abs(ex)<2 else 0
64
+ else:t[:2]=0
65
+ yaw=obs['low_dim_obs'][24];t[3]=np.clip(((0-yaw+np.pi)%(2*np.pi)-np.pi)*2,-.3,.3)
66
+ if self.good>=25 or self.age>150:
67
+ t[:2]=0;t[3]=0;self.phase('source_settle')
68
+ elif self.state=='source_settle':
69
+ if self.age>45:self.phase('lower')
70
+ elif self.state=='lower':
71
+ t[2]=.6
72
+ if self.age>130:self.phase('align')
73
+ elif self.state=='align':
74
+ if self.age%20==0:
75
+ p=self.blob(tools.image('right_wrist'))
76
+ if p:
77
+ cx,cy,area,w,h=p
78
+ t[13]+=np.clip((36-cx)*.0025,-.035,.035)
79
+ t[2]=np.clip(t[2]+np.clip((area-2300)*.000004,-.007,.007),.56,.68)
80
+ t[12]+=np.clip((cy-41)*.0025,-.035,.035)
81
+ t[13]=np.clip(t[13],-.75,.65);t[12]=np.clip(t[12],-.95,-.3)
82
+ if self.age>220:self.phase('close')
83
+ elif self.state=='close':
84
+ t[20]=1
85
+ if self.age>65:self.phase('lift')
86
+ elif self.state=='lift':
87
+ t[2]=.74
88
+ t[5]=t[12]=-.8;t[8]=t[15]=.8;t[13]=-.42
89
+ # Use the same carrying pose for each block.
90
+ if self.age>130:
91
+ p=self.blob(tools.image('right_wrist'))
92
+ if (not p or p[2]<450) and self.retry<1:
93
+ self.retry+=1;t[20]=0;t[12]=-.6;t[13]=-.42;t[15]=.4;self.phase('lower')
94
+ else:
95
+ self.route=[(-.18,float(obs['low_dim_obs'][22]),0),(-.18,.9,0),(2.2,.9,0),(2.2,-.24,0),(2.2,-.24,np.pi),(2.07,-.24,np.pi)]
96
+ self.after_nav='settle';self.phase('nav')
97
+ elif self.state=='settle':
98
+ if self.age>60:self.phase('place')
99
+ elif self.state=='place':
100
+ t[2]=.62+.065*self.block
101
+ if self.age>140:self.phase('release')
102
+ elif self.state=='release':
103
+ t[20]=0
104
+ if self.age>80:self.phase('withdraw')
105
+ elif self.state=='withdraw':
106
+ t[2]=.74
107
+ if self.age>120:
108
+ self.block+=1
109
+ if self.block>=3:self.phase('finish')
110
+ else:
111
+ sy=[0.,.33,.43][self.block]
112
+ self.route=[(2.22,-.24,np.pi),(2.22,.9,np.pi),(-.18,.9,np.pi),(-.18,.9,0),(-.18,sy,0),(.11,sy,0)]
113
+ self.after_nav='new_grasp';self.phase('nav')
114
+ elif self.state=='new_grasp':
115
+ t[5:12]=[-.6,.405,0,.4,0,0,0]
116
+ t[12:19]=[-.6,-.42,0,.4,0,0,0];self.retry=0
117
+ if self.age>100:self.good=0;self.phase('source_align')
118
+ elif self.state=='finish':
119
+ t[:2]=0;t[3]=0
120
+ # Arm and body targets are moved gradually to avoid ejecting a block.
121
+ rate=np.array([1,1,.0015,1,.003]+[.006]*14+[.06,.06])
122
+ self.a+=np.clip(t-self.a,-rate,rate)
123
+ return self.a.copy()
policies/stack_blocks/gpt61sol-s3/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ seed,success,length,termination,fell
2
+ 620000,0,8750,timeout,0
3
+ 620001,0,8750,timeout,0
4
+ 620002,0,8750,timeout,0
5
+ 620003,0,4514,terminated,0
6
+ 620004,0,2033,terminated,0
7
+ 620005,0,8750,timeout,0
8
+ 620006,0,8750,timeout,0
9
+ 620007,0,8750,timeout,0
10
+ 620008,0,4668,terminated,0
11
+ 620009,0,4166,terminated,0
12
+ 620010,0,8750,timeout,0
13
+ 620011,0,8750,timeout,0
14
+ 620012,0,8750,timeout,0
15
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16
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17
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18
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19
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20
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21
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22
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23
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24
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25
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26
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27
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28
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29
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30
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31
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32
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33
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34
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35
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36
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37
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38
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39
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40
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41
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42
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43
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44
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45
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46
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47
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48
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49
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50
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51
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52
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53
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54
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55
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56
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57
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58
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59
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60
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61
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62
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63
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64
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65
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66
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67
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68
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69
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70
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71
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72
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73
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74
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75
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76
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77
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78
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79
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80
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81
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82
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83
+ 620081,0,8750,timeout,0
84
+ 620082,0,8750,timeout,0
85
+ 620083,0,8750,timeout,0
86
+ 620084,0,8750,timeout,0
87
+ 620085,0,8750,timeout,0
88
+ 620086,0,8750,timeout,0
89
+ 620087,0,8750,timeout,0
90
+ 620088,0,7725,terminated,0
91
+ 620089,0,8015,terminated,0
92
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93
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94
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95
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96
+ 620094,0,8750,timeout,0
97
+ 620095,0,8750,timeout,0
98
+ 620096,0,8750,timeout,0
99
+ 620097,0,8750,timeout,0
100
+ 620098,0,8750,timeout,0
101
+ 620099,0,8750,timeout,0
policies/stack_blocks/gpt61sol-s3/policy.py ADDED
@@ -0,0 +1,162 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Deterministic camera feedback controller for transferring and stacking blocks."""
2
+ import numpy as np
3
+ import cv2
4
+
5
+
6
+ def blobs(im, color='red'):
7
+ r,g,b = [im[:,:,k].astype(np.int16) for k in range(3)]
8
+ if color == 'red':
9
+ mask = (r>95)&(r>1.6*g)&(r>1.6*b)
10
+ else:
11
+ mask = (g>100)&(g>1.25*r)&(g>1.25*b)
12
+ n,labels,stats,centers = cv2.connectedComponentsWithStats(mask.astype(np.uint8))
13
+ return sorted([(int(stats[k,4]),centers[k],stats[k,:4])
14
+ for k in range(1,n) if stats[k,4]>8],key=lambda x:-x[0])
15
+
16
+
17
+ class Policy:
18
+ def reset(self, obs, tools):
19
+ self.a=np.asarray(tools.hold_action(),dtype=float)
20
+ self.a[19:21]=0
21
+ self.state='grasp'
22
+ self.st=0
23
+ self.trip=0
24
+ self.route=0
25
+ self.last_seen=None
26
+ self.dest_y=-.12
27
+ self.track=None
28
+ self.aligned=0
29
+
30
+ def change(self, state):
31
+ self.state=state
32
+ self.st=0
33
+
34
+ def navigate(self, obs, target):
35
+ x,y,z,yaw=obs['low_dim_obs'][21:25]
36
+ tx,ty,tw=target
37
+ dx,dy=tx-x,ty-y
38
+ vel=np.clip(np.array([dx,dy])*1.6,-.45,.45)
39
+ dist=np.hypot(dx,dy)
40
+ if dist<.035:
41
+ vel[:]=0
42
+ elif np.linalg.norm(vel)<.075:
43
+ vel*=.075/np.linalg.norm(vel)
44
+ self.a[0]=np.cos(yaw)*vel[0]+np.sin(yaw)*vel[1]
45
+ self.a[1]=-np.sin(yaw)*vel[0]+np.cos(yaw)*vel[1]
46
+ err=(tw-yaw+np.pi)%(2*np.pi)-np.pi
47
+ self.a[3]=np.clip(err*1.6,-.65,.65)
48
+ return dist<.045 and abs(err)<.05
49
+
50
+ def approach(self, values, rate=.004):
51
+ for k,v in values.items():
52
+ self.a[k]+=np.clip(v-self.a[k],-rate,rate)
53
+
54
+ def act(self, obs, tools):
55
+ a=self.a
56
+ t=self.st
57
+ if self.state=='grasp':
58
+ a[0:2]=0;a[3]=0;a[4]=0;a[20]=0
59
+ if t%3==0:
60
+ cams=[(1,'right_wrist')]
61
+ if self.trip==0:
62
+ cams.insert(0,(0,'left_wrist'))
63
+ for j,cam in cams:
64
+ objects=blobs(tools.image(cam))
65
+ if objects:
66
+ if j==1:
67
+ ref=np.array([42.,28.]) if self.track is None else self.track
68
+ candidates=[v for v in objects if v[0]>35]
69
+ if not candidates:continue
70
+ selected=min(candidates,key=lambda v:np.sum((v[1]-ref)**2))
71
+ if self.track is not None and np.linalg.norm(selected[1]-ref)>20:continue
72
+ area,(x,y),box=selected
73
+ self.track=np.array([x,y])
74
+ else:
75
+ area,(x,y),box=objects[0]
76
+ k=5+7*j
77
+ a[k]=np.clip(a[k]+.0018*(y-34),-1.1,.3)
78
+ a[k+1]=np.clip(a[k+1]+.0018*(42-x),-1.1,1.1)
79
+ a[k+4]=-a[k+1]
80
+ a[k+5]=-a[k]
81
+ if j==1:self.last_seen=area
82
+ if self.trip>0:
83
+ a[5]=a[12];a[6]=-a[13];a[9]=-a[6];a[10]=-a[5]
84
+ if t>80:a[2]=max(.51,a[2]-.002)
85
+ if t>=550:
86
+ a[20]=1
87
+ self.change('close')
88
+ elif self.state=='close':
89
+ if t>=65:self.change('lift')
90
+ elif self.state=='lift':
91
+ a[2]=min(.78,a[2]+.002)
92
+ if t>=180:
93
+ self.route=0
94
+ self.change('outbound')
95
+ elif self.state=='outbound':
96
+ routes=[(-.10,1.05,0),(2.50,1.05,0),
97
+ (2.50,self.dest_y,-np.pi),(2.10,self.dest_y,-np.pi)]
98
+ if self.navigate(obs,routes[self.route]) or (self.route==3 and t>1300):
99
+ self.route+=1
100
+ if self.route==len(routes):
101
+ a[0:2]=0;a[3]=0
102
+ self.change('reach')
103
+ elif self.state=='reach':
104
+ a[0:2]=0;a[3]=0
105
+ self.approach({2:.78,4:.2,12:-1.3,13:0,14:0,15:.8,
106
+ 16:0,17:.3,18:0},.006)
107
+ if t>240:self.change('lower')
108
+ elif self.state=='lower':
109
+ # Maintain the horizontal reach while setting the block down.
110
+ a[2]=max(.74+.02*self.trip,a[2]-.0007)
111
+ if t>100:
112
+ a[20]=0
113
+ self.change('release')
114
+ elif self.state=='release':
115
+ if t>70:self.change('withdraw')
116
+ elif self.state=='withdraw':
117
+ self.approach({2:.78,4:0,12:-.8,13:-.4,15:0,16:.4,17:.8},.004)
118
+ if t>200:
119
+ self.trip+=1
120
+ if self.trip>=3:self.change('settle')
121
+ else:
122
+ self.route=0
123
+ self.change('return')
124
+ elif self.state=='return':
125
+ sy=.27
126
+ routes=[(2.50,1.05,-np.pi),(.10,1.05,0),(.10,sy,0)]
127
+ if self.navigate(obs,routes[self.route]):
128
+ self.route+=1
129
+ if self.route==len(routes):
130
+ a[0:2]=0;a[3]=0
131
+ self.change('prepare')
132
+ elif self.state=='prepare':
133
+ a[20]=0
134
+ self.approach({2:.74,4:0,5:0,6:0,7:0,8:0,9:0,10:0,11:0,12:0,13:0,14:0,15:0,16:0,17:0,18:0},.004)
135
+ if t>250:
136
+ self.last_seen=None
137
+ self.track=None
138
+ self.aligned=0
139
+ self.change('align_source')
140
+ elif self.state=='align_source':
141
+ yaw=obs['low_dim_obs'][24]
142
+ a[3]=np.clip(1.6*((-yaw+np.pi)%(2*np.pi)-np.pi),-.3,.3)
143
+ if t%5==0:
144
+ objects=[v for v in blobs(tools.image('head')) if v[0]>14 and v[1][1]<60]
145
+ if objects:
146
+ obj=max(objects,key=lambda v:v[1][0])
147
+ x,y=obj[1]
148
+ ex,ey=67-x,25-y
149
+ vx=0 if abs(ey)<4 else np.sign(ey)*max(.07,min(.14,.007*abs(ey)))
150
+ vy=0 if abs(ex)<4 else np.sign(ex)*max(.07,min(.18,.007*abs(ex)))
151
+ a[0]=vx;a[1]=vy
152
+ self.aligned=self.aligned+5 if abs(ex)<4 and abs(ey)<4 else 0
153
+ else:
154
+ a[0]=0;a[1]=-.1
155
+ if self.aligned>=15 or t>140:
156
+ a[0:2]=0;a[3]=0
157
+ self.track=None
158
+ self.change('grasp')
159
+ elif self.state=='settle':
160
+ a[0:2]=0;a[3]=0
161
+ self.st+=1
162
+ return a.copy()
policies/stack_blocks/gpt61terra-s1/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ seed,success,length,termination,fell
2
+ 620000,0,8750,timeout,0
3
+ 620001,0,661,terminated,0
4
+ 620002,0,8750,timeout,0
5
+ 620003,0,8750,timeout,0
6
+ 620004,0,8750,timeout,0
7
+ 620005,0,8750,timeout,0
8
+ 620006,0,8750,timeout,0
9
+ 620007,0,8750,timeout,0
10
+ 620008,0,8750,timeout,0
11
+ 620009,0,8750,timeout,0
12
+ 620010,0,8750,timeout,0
13
+ 620011,0,8750,timeout,0
14
+ 620012,0,8750,timeout,0
15
+ 620013,0,8750,timeout,0
16
+ 620014,0,8750,timeout,0
17
+ 620015,0,736,terminated,0
18
+ 620016,0,8750,timeout,0
19
+ 620017,0,1036,terminated,0
20
+ 620018,0,8750,timeout,0
21
+ 620019,0,8750,timeout,0
22
+ 620020,0,8750,timeout,0
23
+ 620021,0,8750,timeout,0
24
+ 620022,0,474,terminated,0
25
+ 620023,0,8750,timeout,0
26
+ 620024,0,8750,timeout,0
27
+ 620025,0,8750,timeout,0
28
+ 620026,0,8750,timeout,0
29
+ 620027,0,8750,timeout,0
30
+ 620028,0,8750,timeout,0
31
+ 620029,0,8750,timeout,0
32
+ 620030,0,8750,timeout,0
33
+ 620031,0,8750,timeout,0
34
+ 620032,0,8750,timeout,0
35
+ 620033,0,1107,terminated,0
36
+ 620034,0,8750,timeout,0
37
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38
+ 620036,0,8750,timeout,0
39
+ 620037,0,8750,timeout,0
40
+ 620038,0,8750,timeout,0
41
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42
+ 620040,0,8750,timeout,0
43
+ 620041,0,8750,timeout,0
44
+ 620042,0,8750,timeout,0
45
+ 620043,0,8750,timeout,0
46
+ 620044,0,8750,timeout,0
47
+ 620045,0,8750,timeout,0
48
+ 620046,0,8750,timeout,0
49
+ 620047,0,8750,timeout,0
50
+ 620048,0,8750,timeout,0
51
+ 620049,0,8750,timeout,0
52
+ 620050,0,8750,timeout,0
53
+ 620051,0,8750,timeout,0
54
+ 620052,0,8750,timeout,0
55
+ 620053,0,8750,timeout,0
56
+ 620054,0,8750,timeout,0
57
+ 620055,0,8750,timeout,0
58
+ 620056,0,8750,timeout,0
59
+ 620057,0,8750,timeout,0
60
+ 620058,0,8750,timeout,0
61
+ 620059,0,8750,timeout,0
62
+ 620060,0,8750,timeout,0
63
+ 620061,0,8750,timeout,0
64
+ 620062,0,8750,timeout,0
65
+ 620063,0,8750,timeout,0
66
+ 620064,0,8750,timeout,0
67
+ 620065,0,8750,timeout,0
68
+ 620066,0,8750,timeout,0
69
+ 620067,0,8750,timeout,0
70
+ 620068,0,8750,timeout,0
71
+ 620069,0,8750,timeout,0
72
+ 620070,0,8750,timeout,0
73
+ 620071,0,8750,timeout,0
74
+ 620072,0,8750,timeout,0
75
+ 620073,0,8750,timeout,0
76
+ 620074,0,8750,timeout,0
77
+ 620075,0,8750,timeout,0
78
+ 620076,0,8750,timeout,0
79
+ 620077,0,8750,timeout,0
80
+ 620078,0,8750,timeout,0
81
+ 620079,0,8750,timeout,0
82
+ 620080,0,8750,timeout,0
83
+ 620081,0,8750,timeout,0
84
+ 620082,0,8750,timeout,0
85
+ 620083,0,8750,timeout,0
86
+ 620084,0,8750,timeout,0
87
+ 620085,0,8750,timeout,0
88
+ 620086,0,8750,timeout,0
89
+ 620087,0,8750,timeout,0
90
+ 620088,0,8750,timeout,0
91
+ 620089,0,8750,timeout,0
92
+ 620090,0,8750,timeout,0
93
+ 620091,0,8750,timeout,0
94
+ 620092,0,8750,timeout,0
95
+ 620093,0,8750,timeout,0
96
+ 620094,0,8750,timeout,0
97
+ 620095,0,8750,timeout,0
98
+ 620096,0,8750,timeout,0
99
+ 620097,0,8750,timeout,0
100
+ 620098,0,8750,timeout,0
101
+ 620099,0,8750,timeout,0
policies/stack_blocks/gpt61terra-s1/policy.py ADDED
@@ -0,0 +1,76 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Deterministic visual manipulation and waypoint control for stack_blocks."""
2
+ import math
3
+ import numpy as np
4
+
5
+ class Policy:
6
+ def reset(self, obs, tools):
7
+ self.a=np.asarray(tools.hold_action(),dtype=np.float32)
8
+ self.a[19:21]=0
9
+ self.phase='pickup1'; self.start=0; self.route=0
10
+
11
+ def change(self, phase, t):
12
+ self.phase=phase; self.start=t
13
+
14
+ def walk(self, obs, goal):
15
+ q=obs['low_dim_obs']; x,y,yaw=q[21],q[22],q[24]
16
+ dx=goal[0]-x;dy=goal[1]-y
17
+ ang=(goal[2]-yaw+math.pi)%(2*math.pi)-math.pi
18
+ vx=np.clip(.8*dx,-.16,.16); vy=np.clip(.8*dy,-.16,.16)
19
+ if math.hypot(dx,dy)<.06: vx=vy=0
20
+ self.a[0]=vx*math.cos(yaw)+vy*math.sin(yaw)
21
+ self.a[1]=-vx*math.sin(yaw)+vy*math.cos(yaw)
22
+ self.a[3]=np.clip(.8*ang,-.3,.3) if abs(ang)>.06 else 0
23
+ return math.hypot(dx,dy)<.08 and abs(ang)<.09
24
+
25
+ def act(self, obs, tools):
26
+ t=obs['t']; age=t-self.start; q=obs['low_dim_obs']
27
+ a=self.a
28
+ if self.phase=='pickup1':
29
+ a[6]=.35;a[13]=-.35
30
+ if age<90: a[0]=.12 if q[21]<.24 else 0
31
+ elif age<180: a[0]=0;a[2]=.65
32
+ elif age<270: a[19:21]=1
33
+ elif age<370:a[2]=.74
34
+ else:self.route=0;self.change('out1',t)
35
+ elif self.phase in ('out1','out2'):
36
+ goals=[(-.07,0,0),(-.07,-.78,0),(1.35,-.78,0),(1.35,-.78,1.6)]
37
+ if self.walk(obs,goals[self.route]):
38
+ self.route+=1
39
+ if self.route==len(goals):a[0:2]=0;a[3]=0;self.change('place1' if self.phase=='out1' else 'place3',t)
40
+ elif self.phase=='place1':
41
+ a[0:2]=0;a[3]=0
42
+ z=min(1.,age/300.)
43
+ a[6]=.35*(1-z);a[13]=-.35*(1-z)
44
+ a[5]=a[12]=-.6*z;a[8]=a[15]=z
45
+ if age>375:
46
+ w=min(1.,(age-375)/150.);a[5]=a[12]=-.6-.65*w;a[8]=a[15]=1.-.9*w
47
+ if age>550:a[2]=.65
48
+ if age>650:a[19]=0
49
+ if age>825:
50
+ w=min(1.,(age-825)/150.);a[5]=-.8-.4*w;a[8]=.7+.5*w
51
+ if age>1050:
52
+ w=min(1.,(age-1050)/180.);a[13]=.27*w
53
+ if age>1250:a[20]=0
54
+ if age>1400:
55
+ a[2]=.74;a[5:19]=0;a[19:21]=0
56
+ if age>1500:self.route=0;self.change('back',t)
57
+ elif self.phase=='back':
58
+ goals=[(1.35,-.85,0),(-.07,-.8,0),(-.07,0,0),(.24,0,0)]
59
+ if self.walk(obs,goals[self.route]):
60
+ self.route+=1
61
+ if self.route==len(goals):a[0:2]=0;a[3]=0;self.change('pickup3',t)
62
+ elif self.phase=='pickup3':
63
+ a[6]=.5;a[13]=0
64
+ if age<70:a[0]=.12 if q[21]<.24 else 0
65
+ elif age<160:a[0]=0;a[2]=.65
66
+ elif age<250:a[20]=1
67
+ elif age<350:a[2]=.74
68
+ else:self.route=0;self.change('out2',t)
69
+ elif self.phase=='place3':
70
+ a[0:2]=0;a[3]=0
71
+ z=min(1.,age/300.)
72
+ a[13]=.27*z;a[12]=-1.25*z;a[15]=.1*z
73
+ if age>350:a[2]=.68
74
+ if age>450:a[20]=0
75
+ if age>600:a[12]=-1.2;a[15]=1.2
76
+ return a.copy()
policies/stack_blocks/gpt61terra-s2/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ seed,success,length,termination,fell
2
+ 620000,0,8750,timeout,0
3
+ 620001,0,1555,terminated,0
4
+ 620002,0,8750,timeout,0
5
+ 620003,0,8750,timeout,0
6
+ 620004,0,8750,timeout,0
7
+ 620005,0,8750,timeout,0
8
+ 620006,0,8750,timeout,0
9
+ 620007,0,8750,timeout,0
10
+ 620008,0,8750,timeout,0
11
+ 620009,0,8750,timeout,0
12
+ 620010,0,8750,timeout,0
13
+ 620011,0,8750,timeout,0
14
+ 620012,0,8750,timeout,0
15
+ 620013,0,8750,timeout,0
16
+ 620014,0,8750,timeout,0
17
+ 620015,0,8750,timeout,0
18
+ 620016,0,8750,timeout,0
19
+ 620017,0,8750,timeout,0
20
+ 620018,0,8750,timeout,0
21
+ 620019,0,8750,timeout,0
22
+ 620020,0,8750,timeout,0
23
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24
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25
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26
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27
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28
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29
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30
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31
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32
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33
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34
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35
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36
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37
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38
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39
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40
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41
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42
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43
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44
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45
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46
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47
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48
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49
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50
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51
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52
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53
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54
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55
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56
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57
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58
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59
+ 620057,0,8750,timeout,0
60
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61
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62
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63
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64
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65
+ 620063,0,8750,timeout,0
66
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67
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68
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69
+ 620067,0,8750,timeout,0
70
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71
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72
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73
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74
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75
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76
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77
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78
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79
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80
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81
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82
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83
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84
+ 620082,0,2335,terminated,0
85
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86
+ 620084,0,8750,timeout,0
87
+ 620085,0,8750,timeout,0
88
+ 620086,0,8750,timeout,0
89
+ 620087,0,8750,timeout,0
90
+ 620088,0,8750,timeout,0
91
+ 620089,0,8750,timeout,0
92
+ 620090,0,8750,timeout,0
93
+ 620091,0,8750,timeout,0
94
+ 620092,0,8750,timeout,0
95
+ 620093,0,8750,timeout,0
96
+ 620094,0,8750,timeout,0
97
+ 620095,0,8750,timeout,0
98
+ 620096,0,8750,timeout,0
99
+ 620097,0,8750,timeout,0
100
+ 620098,0,8750,timeout,0
101
+ 620099,0,8750,timeout,0
policies/stack_blocks/gpt61terra-s2/policy.py ADDED
@@ -0,0 +1,108 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Deterministic vision-assisted block transfer controller."""
2
+ import math
3
+ import numpy as np
4
+ import cv2
5
+
6
+ class Policy:
7
+ def reset(self, obs, tools):
8
+ self.a=np.asarray(tools.hold_action(),dtype=np.float32)
9
+ self.phase='pickup'; self.enter=0
10
+ self.left_roll=.25; self.right_roll=-.15
11
+ im=tools.image('head')
12
+ mask=((im[:,:,0]>130)&(im[:,:,0]>im[:,:,1]*1.7)&(im[:,:,0]>im[:,:,2]*1.7)).astype(np.uint8)
13
+ n, lab, stats, cent=cv2.connectedComponentsWithStats(mask)
14
+ blobs=sorted([cent[i,0] for i in range(1,n) if stats[i,4]>=7])
15
+ if len(blobs)>=3:
16
+ self.left_roll=float(np.clip(.25-(blobs[0]-15)*.012,.05,.45))
17
+ self.right_roll=float(np.clip(-.15-(blobs[-1]-66)*.012,-.4,.05))
18
+ self.a[19:21]=0
19
+ self.a[6]=self.left_roll;self.a[13]=self.right_roll
20
+ def switch(self,p,t):
21
+ self.phase=p; self.enter=t
22
+ def act(self,obs,tools):
23
+ t=obs['t']; x,y,z,yaw=map(float,obs['low_dim_obs'][21:25]); dt=t-self.enter
24
+ a=self.a.copy();a[0]=a[1]=a[3]=0
25
+ phase=self.phase
26
+ if phase=='pickup':
27
+ a[0]=.1 if 30<=dt<85 else 0
28
+ if dt>=100:a[2]=.65
29
+ if dt==140:a[5]=a[12]=-.1
30
+ if 170<=dt<320 and dt%10==0:
31
+ for camera, pi, ri, wanted in [('left_wrist',5,6,35),('right_wrist',12,13,45)]:
32
+ im=tools.image(camera)
33
+ red=(im[:,:,0]>120)&(im[:,:,0]>1.6*im[:,:,1])&(im[:,:,0]>1.6*im[:,:,2])
34
+ yy,xx=np.nonzero(red)
35
+ if len(xx)>20:
36
+ dx=wanted-float(np.mean(xx)); dy=float(np.mean(yy))-35
37
+ if abs(dx)>8:a[ri]=np.clip(a[ri]+np.clip(dx*.0015,-.025,.025),-.5,.55)
38
+ if abs(dy)>10:a[pi]=np.clip(a[pi]+np.clip(dy*.0015,-.025,.025),-.3,.25)
39
+ if dt>=340:a[19:21]=1
40
+ if dt>=470:a[2]=.74
41
+ if dt>=620:self.switch('out_side',t)
42
+ elif phase=='out_side':
43
+ a[1]=-.25; a[3]=np.clip(-yaw,-.3,.3)
44
+ if y<-.95 or dt>450:self.switch('out_forward',t)
45
+ elif phase=='out_forward':
46
+ a[0]=.25; a[3]=np.clip(-yaw,-.3,.3)
47
+ if x>1.25 or dt>450:self.switch('turn_pad',t)
48
+ elif phase=='turn_pad':
49
+ a[3]=np.clip(1.57-yaw,-.3,.3)
50
+ if yaw>1.50 or dt>450:self.switch('approach_pad',t)
51
+ elif phase=='approach_pad':
52
+ a[0]=.12;a[3]=np.clip(1.57-yaw,-.3,.3)
53
+ if y>-.68 or dt>300:self.switch('first_place',t)
54
+ elif phase=='first_place':
55
+ a[4]=.8;a[12]=-.30;a[13]=-.15
56
+ if dt>200:a[2]=.70
57
+ if dt>400:a[20]=0
58
+ if dt>550:self.switch('shift',t)
59
+ elif phase=='shift':
60
+ a[4]=0;a[2]=.74;a[12]=-.6
61
+ if dt>100:
62
+ a[1]=-.12;a[3]=np.clip(1.57-yaw,-.3,.3)
63
+ if (dt>100 and x>1.57) or dt>300:self.switch('second_place',t)
64
+ elif phase=='second_place':
65
+ a[4]=.45;a[5]=-.3;a[6]=-.35
66
+ if dt>180:a[2]=.73
67
+ if dt>350:a[19]=0
68
+ if dt>500:self.switch('back_from_pad',t)
69
+ elif phase=='back_from_pad':
70
+ a[2]=.74;a[4]=0;a[5]=a[12]=-.1;a[6]=.25;a[13]=-.15
71
+ if dt>100:a[0]=-.20
72
+ a[3]=np.clip(1.57-yaw,-.3,.3)
73
+ if y<-.98 or dt>400:self.switch('turn_home',t)
74
+ elif phase=='turn_home':
75
+ a[3]=np.clip(-yaw,-.3,.3)
76
+ if yaw<.07 or dt>400:self.switch('home_forward',t)
77
+ elif phase=='home_forward':
78
+ a[0]=-.25;a[3]=np.clip(-yaw,-.3,.3)
79
+ if x<.22 or dt>500:self.switch('home_side',t)
80
+ elif phase=='home_side':
81
+ a[1]=.22;a[3]=np.clip(-yaw,-.3,.3)
82
+ if y>.12 or dt>500:self.switch('third_pick',t)
83
+ elif phase=='third_pick':
84
+ a[2]=.74;a[4]=0;a[12]=0;a[13]=-.15;a[20]=0
85
+ if dt>100:a[0]=.1
86
+ if dt>150:a[0]=0
87
+ if dt>200:a[2]=.65
88
+ if dt>240:a[12]=-.1
89
+ if dt>350:a[20]=1
90
+ if dt>450:a[2]=.74
91
+ if dt>600:self.switch('return_side',t)
92
+ elif phase=='return_side':
93
+ a[1]=-.25;a[3]=np.clip(-yaw,-.3,.3)
94
+ if y<-.95 or dt>500:self.switch('return_forward',t)
95
+ elif phase=='return_forward':
96
+ a[0]=.25;a[3]=np.clip(-yaw,-.3,.3)
97
+ if x>1.25 or dt>500:self.switch('return_turn',t)
98
+ elif phase=='return_turn':
99
+ a[3]=np.clip(1.57-yaw,-.3,.3)
100
+ if yaw>1.5 or dt>450:self.switch('return_approach',t)
101
+ elif phase=='return_approach':
102
+ a[0]=.12;a[3]=np.clip(1.57-yaw,-.3,.3)
103
+ if y>-.68 or dt>300:self.switch('third_place',t)
104
+ elif phase=='third_place':
105
+ a[4]=.5;a[12]=-.3;a[13]=-.15
106
+ if dt>300:a[20]=0
107
+ self.a=a.copy()
108
+ return a
policies/stack_blocks/gpt61terra-s3/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ seed,success,length,termination,fell
2
+ 620000,0,8750,timeout,0
3
+ 620001,0,1267,terminated,0
4
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5
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6
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7
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8
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9
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13
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14
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15
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21
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22
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23
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24
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31
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37
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40
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42
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43
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44
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50
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51
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52
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54
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55
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56
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58
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60
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61
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62
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63
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64
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65
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66
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67
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68
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69
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70
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71
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72
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73
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74
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75
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76
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78
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84
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85
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86
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87
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88
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89
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90
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91
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92
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93
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94
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95
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96
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97
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98
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99
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100
+ 620098,0,8750,timeout,0
101
+ 620099,0,8750,timeout,0
policies/stack_blocks/gpt61terra-s3/policy.py ADDED
@@ -0,0 +1,136 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import math
2
+ import numpy as np
3
+ import cv2
4
+
5
+ class Policy:
6
+ def reset(self, obs, tools):
7
+ self.a = np.asarray(tools.hold_action(), dtype=np.float32)
8
+ self.a[19:21] = 0
9
+ self.a[6] = .22
10
+ self.a[13] = -.22
11
+ self.third_x = .2
12
+ self.third_y = .17
13
+ self.approach_steps = 35
14
+ im = tools.image('head')
15
+ red = ((im[:,:,0] > 130) & (im[:,:,0] > im[:,:,1]*1.7) & (im[:,:,0] > im[:,:,2]*1.7)).astype(np.uint8)
16
+ n, labels, stats, centers = cv2.connectedComponentsWithStats(red)
17
+ blobs = sorted([(centers[i][0], centers[i][1], stats[i,4]) for i in range(1,n) if stats[i,4] >= 25], key=lambda p:p[0])
18
+ if len(blobs) >= 3:
19
+ left, mid, right = blobs[0], blobs[len(blobs)//2], blobs[-1]
20
+ self.a[6] = np.clip(.22 + (13.3-left[0])*.020, -.15, .50)
21
+ self.a[13] = np.clip(-.22 + (66.5-right[0])*.020, -.50, .15)
22
+ self.approach_steps = int(round(np.clip(35 - ((left[1]+right[1])*.5-25.0)*2.5, 17, 50)))
23
+ self.third_x = float(np.clip(.2 - (mid[1]-26.7)*.010, .12, .26))
24
+ self.third_y = float(np.clip(.17 - (mid[0]-36.2)*.010, -.05, .38))
25
+ self.stage = 0
26
+ self.n = 0
27
+ self.waypoints = []
28
+ self.point = 0
29
+ self.wait = 0
30
+ self.point_steps = 0
31
+
32
+ def next(self):
33
+ self.stage += 1
34
+ self.n = 0
35
+
36
+ def path(self, points):
37
+ self.waypoints = points
38
+ self.point = 0
39
+ self.wait = 0
40
+ self.point_steps = 0
41
+
42
+ def navigate(self, obs):
43
+ if self.point >= len(self.waypoints):
44
+ self.a[0:2]=0; self.a[3]=0
45
+ return True
46
+ x,y,yaw = self.waypoints[self.point]
47
+ X,Y,_,th = obs['low_dim_obs'][21:25]
48
+ dx,dy=x-X,y-Y
49
+ ang=(yaw-th+math.pi)%(2*math.pi)-math.pi
50
+ dist=math.hypot(dx,dy)
51
+ self.point_steps += 1
52
+ if (dist < .055 and abs(ang)<.075) or (self.point_steps > 350 and dist < .09 and abs(ang) < .13):
53
+ self.a[0:2]=0;self.a[3]=0
54
+ self.wait+=1
55
+ if self.wait>=30:
56
+ self.point+=1; self.wait=0; self.point_steps=0
57
+ return False
58
+ self.wait=0
59
+ v=np.clip(np.array([dx,dy])*1.0,-.27,.27)
60
+ self.a[0]=math.cos(th)*v[0]+math.sin(th)*v[1]
61
+ self.a[1]=-math.sin(th)*v[0]+math.cos(th)*v[1]
62
+ if dist<.05:self.a[0:2]=0
63
+ self.a[3]=np.clip(ang*1.3,-.5,.5)
64
+ return False
65
+
66
+ def act(self, obs, tools):
67
+ s=self.stage; self.n+=1
68
+ if s==0:
69
+ self.a[0]=.2
70
+ if self.n>=self.approach_steps:self.a[0]=0;self.next()
71
+ elif s==1:
72
+ self.a[2]=.63
73
+ if self.n>=150:self.next()
74
+ elif s==2:
75
+ self.a[19:21]=1
76
+ if self.n>=100:self.next()
77
+ elif s==3:
78
+ self.a[2]=.74
79
+ if self.n>=150:
80
+ self.path([(.2,0,1.57),(.2,.9,1.57),(.2,.9,0),(2.7,.9,0),(2.7,.9,-1.57),(2.7,0,-1.57),(2.7,0,3.14),(1.9,.18,3.14)]);self.next()
81
+ elif s==4:
82
+ if self.navigate(obs):self.next()
83
+ elif s==5:
84
+ self.a[2]=.63
85
+ if self.n>=150:self.next()
86
+ elif s==6:
87
+ self.a[19]=0
88
+ if self.n>=120:self.next()
89
+ elif s==7:
90
+ self.a[2]=.74;self.a[5]=-.5
91
+ if self.n>=150:self.path([(1.9,-.32,3.14)]);self.next()
92
+ elif s==8:
93
+ if self.navigate(obs):self.next()
94
+ elif s==9:
95
+ self.a[2]=.69
96
+ if self.n>=150:self.next()
97
+ elif s==10:
98
+ self.a[20]=0
99
+ if self.n>=120:self.next()
100
+ elif s==11:
101
+ self.a[2]=.74;self.a[12]=-.5
102
+ if self.n>=150:
103
+ self.path([(2.7,-.32,3.14),(2.7,.9,1.57),(.2,.9,3.14),(.2,.9,0),(self.third_x,self.third_y,0)]);self.next()
104
+ elif s==12:
105
+ if self.navigate(obs):self.next()
106
+ elif s==13:
107
+ self.a[12]=0
108
+ if self.n < 40:
109
+ self.a[13]=-.22
110
+ elif self.n % 10 == 0:
111
+ im = tools.image('right_wrist')
112
+ mask = ((im[:,:,0] > 130) & (im[:,:,0] > im[:,:,1]*1.7) & (im[:,:,0] > im[:,:,2]*1.7)).astype(np.uint8)
113
+ count, labels, stats, centers = cv2.connectedComponentsWithStats(mask)
114
+ candidates = [i for i in range(1,count) if stats[i,4] >= 20]
115
+ if candidates:
116
+ i = max(candidates, key=lambda j:stats[j,4])
117
+ self.a[13] = np.clip(self.a[13] + np.clip((35-centers[i,0])*.002, -.035, .035), -.5, .15)
118
+ if self.n>=120:self.next()
119
+ elif s==14:
120
+ self.a[2]=.63
121
+ if self.n>=150:self.next()
122
+ elif s==15:
123
+ self.a[20]=1
124
+ if self.n>=120:self.next()
125
+ elif s==16:
126
+ self.a[2]=.74
127
+ if self.n>=150:
128
+ self.path([(.2,.9,1.57),(.2,.9,0),(2.7,.9,0),(2.7,0,-1.57),(2.7,0,3.14),(1.9,-.32,3.14)]);self.next()
129
+ elif s==17:
130
+ if self.navigate(obs):self.next()
131
+ elif s==18:
132
+ self.a[20]=0
133
+ if self.n>=120:self.next()
134
+ elif s==19:
135
+ self.a[12]=-.5
136
+ return self.a.copy()
policies/stack_blocks/gpt6sol-s1/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
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2
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58
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61
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62
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63
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64
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65
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66
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70
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78
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80
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policies/stack_blocks/gpt6sol-s1/policy.py ADDED
@@ -0,0 +1,198 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Deterministic whole-body script for the block stacking task."""
2
+
3
+ import math
4
+ import numpy as np
5
+ import cv2
6
+
7
+
8
+ def angle_error(target, actual):
9
+ return math.atan2(math.sin(target - actual), math.cos(target - actual))
10
+
11
+
12
+ class Policy:
13
+ def reset(self, obs, tools):
14
+ self.a = np.asarray(tools.hold_action(), dtype=np.float32).copy()
15
+ self.a[19:21] = 0.0
16
+ self.phase = 0
17
+ self.clock = 0
18
+ self.entered = False
19
+ self.stages = []
20
+
21
+ def hold(n, edits=None):
22
+ self.stages.append(('hold', n, edits or {}))
23
+
24
+ def turn(yaw):
25
+ self.stages.append(('turn', yaw, {}))
26
+ hold(20, {3: 0})
27
+
28
+ def walk(axis, target, yaw, speed=.28):
29
+ self.stages.append(('walk', (axis, target, yaw, speed), {}))
30
+ hold(25, {0: 0, 1: 0, 3: 0})
31
+
32
+ def strafe(target, yaw):
33
+ self.stages.append(('strafe', (target, yaw), {}))
34
+ hold(20, {0: 0, 1: 0, 3: 0})
35
+
36
+ def back(target):
37
+ self.stages.append(('back', target, {}))
38
+ hold(20, {0: 0, 1: 0, 3: 0})
39
+
40
+ def adjust(x, y):
41
+ for _ in range(3):
42
+ self.stages.append(('adjust', (x, y), {}))
43
+ hold(20, {0: 0, 1: 0, 3: 0})
44
+
45
+ def align(camera, joint, target, pixels_per_radian):
46
+ self.stages.append(('align', (camera, joint, target, pixels_per_radian), {}))
47
+
48
+ # Grasp the two outside blocks, one hand at a time.
49
+ self.stages.append(('approach', .225, {}))
50
+ hold(30)
51
+ hold(60, {5: -.3, 6: .3, 10: .9})
52
+ align('left_wrist', 6, 50, 245)
53
+ hold(60, {19: 1})
54
+ hold(60, {5: -.5, 10: 1.2})
55
+ hold(60, {12: -.3, 13: -.3, 17: .9})
56
+ align('right_wrist', 13, 40, 90)
57
+ hold(60, {20: 1})
58
+ hold(60, {12: -.5, 17: 1.2})
59
+
60
+ # Pass north of both tables, then approach the east side of the pad.
61
+ strafe(1.2, 0)
62
+ walk('x', 2.3, 0)
63
+ strafe(0, 0)
64
+ turn(math.pi)
65
+ walk('x', 1.85, math.pi)
66
+ adjust(1.94, .24)
67
+
68
+ # Place the first block.
69
+ hold(60, {6: -.35})
70
+ hold(80, {19: 0})
71
+ hold(60, {5: -.3, 6: .3, 10: .9})
72
+ hold(80)
73
+
74
+ # Move the base so the right hand is over the placed block.
75
+ adjust(1.95, -.20)
76
+ hold(90, {20: 0})
77
+ hold(60, {12: -.8})
78
+ hold(90)
79
+
80
+ # Return for the remaining center block.
81
+ back(2.3)
82
+ strafe(1.2, math.pi)
83
+ walk('x', .05, math.pi)
84
+ strafe(-.14, math.pi)
85
+ adjust(.08, -.14)
86
+ turn(0)
87
+ adjust(.08, -.14)
88
+ self.stages.append(('approach', .225, {}))
89
+ hold(30)
90
+ hold(60, {5: -.3, 6: .3, 10: .9})
91
+ align('left_wrist', 6, 23, 245)
92
+ hold(60, {19: 1})
93
+ hold(60, {5: -.5, 10: 1.2})
94
+
95
+ # Carry the center block to the stack.
96
+ strafe(1.2, 0)
97
+ walk('x', 2.3, 0)
98
+ strafe(0, 0)
99
+ turn(math.pi)
100
+ walk('x', 1.85, math.pi)
101
+ adjust(1.94, .24)
102
+ hold(60, {5: -.7, 6: -.35})
103
+ hold(80, {19: 0})
104
+ hold(60, {5: -.3, 6: .3, 10: .9})
105
+ hold(150)
106
+
107
+ def act(self, obs, tools):
108
+ pose = obs['low_dim_obs']
109
+ while self.phase < len(self.stages):
110
+ kind, param, edits = self.stages[self.phase]
111
+ if not self.entered:
112
+ for i, value in edits.items():
113
+ self.a[i] = value
114
+ if kind == 'align':
115
+ camera, joint, target, scale = param
116
+ image = tools.image(camera)
117
+ red = ((image[:, :, 0] > 100) &
118
+ (image[:, :, 0] > 1.7 * image[:, :, 1]) &
119
+ (image[:, :, 0] > 1.7 * image[:, :, 2])).astype(np.uint8)
120
+ n, _, stats, centers = cv2.connectedComponentsWithStats(red, 8)
121
+ if n > 1:
122
+ i = 1 + int(np.argmax(stats[1:, 4]))
123
+ if stats[i, 4] > 350:
124
+ delta = np.clip((target - centers[i, 0]) / scale, -.15, .15)
125
+ self.a[joint] += delta
126
+ self.entered = True
127
+ self.clock = 0
128
+
129
+ finished = False
130
+ if kind == 'hold':
131
+ if 0 not in edits:
132
+ self.a[0] = 0
133
+ if 1 not in edits:
134
+ self.a[1] = 0
135
+ if 3 not in edits:
136
+ self.a[3] = 0
137
+ finished = self.clock >= param
138
+ elif kind == 'align':
139
+ self.a[0:2] = 0
140
+ self.a[3] = 0
141
+ finished = self.clock >= 35
142
+ elif kind == 'approach':
143
+ self.a[0] = .20
144
+ self.a[1] = 0
145
+ self.a[3] = 0
146
+ finished = float(pose[21]) > param or self.clock > 250
147
+ elif kind == 'turn':
148
+ err = angle_error(param, float(pose[24]))
149
+ self.a[0:2] = 0
150
+ turn_limit = .35 if self.a[19] > .5 or self.a[20] > .5 else .8
151
+ self.a[3] = np.clip(err, -turn_limit, turn_limit)
152
+ finished = abs(err) < .025 or self.clock > 600
153
+ elif kind == 'strafe':
154
+ target, yaw = param
155
+ coord = float(pose[22])
156
+ if self.clock == 0:
157
+ self.direction = 1 if target > coord else -1
158
+ self.a[0] = 0
159
+ self.a[1] = self.direction * .5 * math.cos(float(pose[24]))
160
+ self.a[3] = np.clip(.8 * angle_error(yaw, float(pose[24])), -.25, .25)
161
+ finished = self.direction * (target - coord) < .02 or self.clock > 500
162
+ elif kind == 'back':
163
+ self.a[0] = -.35
164
+ self.a[1] = 0
165
+ self.a[3] = np.clip(.8 * angle_error(math.pi, float(pose[24])), -.25, .25)
166
+ finished = float(pose[21]) > param - .02 or self.clock > 200
167
+ elif kind == 'walk':
168
+ axis, target, yaw, speed = param
169
+ coord = float(pose[21 if axis == 'x' else 22])
170
+ if self.clock == 0:
171
+ self.direction = 1 if target > coord else -1
172
+ self.a[0] = speed
173
+ self.a[1] = 0
174
+ self.a[3] = np.clip(.8 * angle_error(yaw, float(pose[24])), -.2, .2)
175
+ finished = self.direction * (target - coord) < .015 or self.clock > 1000
176
+ else:
177
+ x, y = param
178
+ ex, ey = x - float(pose[21]), y - float(pose[22])
179
+ yaw = float(pose[24])
180
+ vx = math.cos(yaw) * ex + math.sin(yaw) * ey
181
+ vy = -math.sin(yaw) * ex + math.cos(yaw) * ey
182
+ self.a[0] = np.clip(1.2 * vx, -.2, .2)
183
+ self.a[1] = np.clip(1.2 * vy, -.2, .2)
184
+ for i, error in ((0, vx), (1, vy)):
185
+ if .025 < abs(error) and abs(self.a[i]) < .07:
186
+ self.a[i] = math.copysign(.07, self.a[i])
187
+ self.a[3] = 0
188
+ finished = (abs(ex) < .025 and abs(ey) < .025) or self.clock > 250
189
+
190
+ if finished:
191
+ self.phase += 1
192
+ self.entered = False
193
+ continue
194
+ self.clock += 1
195
+ return self.a.copy()
196
+ self.a[0:2] = 0
197
+ self.a[3] = 0
198
+ return self.a.copy()
policies/stack_blocks/gpt6sol-s2/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ seed,success,length,termination,fell
2
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3
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4
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5
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7
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8
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9
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10
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11
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12
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13
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14
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15
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16
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17
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18
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19
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20
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21
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22
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23
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24
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25
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28
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29
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31
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35
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37
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38
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40
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41
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42
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43
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44
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45
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46
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47
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48
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49
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50
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51
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52
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54
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55
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56
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57
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58
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59
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60
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61
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62
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63
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64
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65
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66
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67
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68
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69
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70
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71
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72
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73
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74
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78
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79
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80
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81
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82
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83
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84
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85
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86
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87
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88
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89
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90
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91
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92
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93
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94
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95
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96
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97
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98
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99
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100
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101
+ 620099,0,2138,terminated,0
policies/stack_blocks/gpt6sol-s2/policy.py ADDED
@@ -0,0 +1,156 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Deterministic camera and proprioceptive controller for the block task."""
2
+
3
+ import math
4
+ import numpy as np
5
+
6
+
7
+ def _wrap(v):
8
+ return (v + math.pi) % (2 * math.pi) - math.pi
9
+
10
+
11
+ class Policy:
12
+ def reset(self, obs, tools):
13
+ self.a = np.asarray(tools.hold_action(), dtype=np.float32)
14
+ self.a[2] = 0.74
15
+ self.a[4] = 0
16
+ self.a[5:19] = 0
17
+ self.a[5] = self.a[12] = 0.2
18
+ self.a[6] = 0.3
19
+ self.a[13] = -0.3
20
+ self.a[19:21] = 0
21
+ self.phase = 0
22
+ self.age = 0
23
+ self.near = 0
24
+ self.third_y = None
25
+ self.grab_tries = 0
26
+
27
+ def _nav(self, obs, x, y, heading, speed=0.28):
28
+ q = obs["low_dim_obs"]
29
+ dx = x - float(q[21])
30
+ dy = y - float(q[22])
31
+ yaw = float(q[24])
32
+ err = _wrap(heading - yaw)
33
+ near = math.hypot(dx, dy) < 0.07 and abs(err) < 0.05
34
+ if near:
35
+ self.a[0] = self.a[1] = self.a[3] = 0
36
+ self.near += 1
37
+ else:
38
+ self.near = 0
39
+ v = np.clip(np.array([dx, dy]) * 1.5, -speed, speed)
40
+ c = math.cos(yaw)
41
+ s = math.sin(yaw)
42
+ self.a[0] = c * v[0] + s * v[1]
43
+ self.a[1] = -s * v[0] + c * v[1]
44
+ self.a[3] = np.clip(1.5 * err, -0.4, 0.4)
45
+ if math.hypot(dx, dy) < 0.07:
46
+ self.a[0:2] = 0
47
+ return self.near >= 30 or self.age > 850
48
+
49
+ def act(self, obs, tools):
50
+ self.a[0] = self.a[1] = self.a[3] = 0
51
+ p = self.phase
52
+ done = False
53
+ if p == 0:
54
+ done = self.age >= 70
55
+ elif p == 1:
56
+ self.a[0] = 0.12
57
+ done = self.age >= 100
58
+ elif p == 2:
59
+ self.a[19:21] = 1
60
+ done = self.age >= 70
61
+ elif p == 3:
62
+ self.a[0] = -0.2
63
+ done = self.age >= 140
64
+ elif p == 4:
65
+ done = self._nav(obs, 0, -1.5, 0)
66
+ elif p == 5:
67
+ self.a[5] = self.a[12] = -0.2
68
+ done = self._nav(obs, 2.25, -1.5, 0)
69
+ elif p == 6:
70
+ done = self._nav(obs, 2.04, -0.07, math.pi)
71
+ elif p == 7:
72
+ self.a[12] = 0
73
+ self.a[13] = 0.3
74
+ done = self.age >= 100
75
+ elif p == 8:
76
+ self.a[20] = 0
77
+ done = self.age >= 120
78
+ elif p == 9:
79
+ self.a[12] = -0.2
80
+ self.a[13] = -0.3
81
+ done = self._nav(obs, 2.04, 0.25, math.pi, 0.2)
82
+ elif p == 10:
83
+ self.a[5] = -0.1
84
+ self.a[6] = -0.1
85
+ done = self.age >= 100
86
+ elif p == 11:
87
+ self.a[19] = 0
88
+ done = self.age >= 120
89
+ elif p == 12:
90
+ self.a[5:19] = 0
91
+ done = self._nav(obs, 2.25, -1.5, math.pi)
92
+ elif p == 13:
93
+ done = self._nav(obs, 0, -1.5, math.pi)
94
+ elif p == 14:
95
+ q = obs["low_dim_obs"]
96
+ if self.third_y is None and math.hypot(float(q[21]) - 0.1, float(q[22]) + 0.08) < 0.1 and abs(_wrap(float(q[24]))) < 0.1:
97
+ im = tools.image("head")
98
+ rr = im[:, :, 0].astype(np.int16)
99
+ gg = im[:, :, 1].astype(np.int16)
100
+ bb = im[:, :, 2].astype(np.int16)
101
+ mask = (rr > 140) & (rr > gg * 1.7) & (rr > bb * 1.5)
102
+ mask[45:, :] = False
103
+ xs = np.where(mask)[1]
104
+ if len(xs) >= 5:
105
+ self.third_y = float(np.clip(float(q[22]) + (12.0 - float(np.median(xs))) / 140.0, -0.3, 0.15))
106
+ else:
107
+ self.third_y = -0.08
108
+ self.near = 0
109
+ done = self._nav(obs, 0.1, self.third_y if self.third_y is not None else -0.08, 0)
110
+ elif p == 15:
111
+ self.a[5] = 0.2
112
+ self.a[6] = 0.1 - 0.1 * self.grab_tries
113
+ self.a[19] = 0
114
+ done = self.age >= 70
115
+ elif p == 16:
116
+ self.a[0] = 0.12
117
+ done = self.age >= 60 - 10 * self.grab_tries
118
+ elif p == 17:
119
+ self.a[19] = 1
120
+ done = self.age >= 70
121
+ elif p == 18:
122
+ self.a[0] = -0.2
123
+ done = self.age >= 140
124
+ elif p == 19:
125
+ if self.age == 0 and self.grab_tries < 2:
126
+ im = tools.image("left_wrist")
127
+ rr = im[:, :, 0].astype(np.int16)
128
+ gg = im[:, :, 1].astype(np.int16)
129
+ bb = im[:, :, 2].astype(np.int16)
130
+ held_pixels = int(((rr > 140) & (rr > gg * 1.7) & (rr > bb * 1.5)).sum())
131
+ if held_pixels < 450:
132
+ self.grab_tries += 1
133
+ self.third_y = None
134
+ self.phase = 14
135
+ self.a[19] = 0
136
+ return self.a.copy()
137
+ done = self._nav(obs, 0, -1.5, 0)
138
+ elif p == 20:
139
+ self.a[5] = -0.2
140
+ done = self._nav(obs, 2.25, -1.5, 0)
141
+ elif p == 21:
142
+ done = self._nav(obs, 2.04, 0.25, math.pi)
143
+ elif p == 22:
144
+ self.a[5] = -0.1
145
+ self.a[6] = -0.1
146
+ done = self.age >= 100
147
+ elif p == 23:
148
+ self.a[19] = 0
149
+ done = self.age >= 120
150
+ if done:
151
+ self.phase += 1
152
+ self.age = 0
153
+ self.near = 0
154
+ else:
155
+ self.age += 1
156
+ return self.a.copy()
policies/stack_blocks/gpt6sol-s3/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ seed,success,length,termination,fell
2
+ 620000,0,4410,terminated,0
3
+ 620001,0,966,terminated,0
4
+ 620002,0,8750,timeout,0
5
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6
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7
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8
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9
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10
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11
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12
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13
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14
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15
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16
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17
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18
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19
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20
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21
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22
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23
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24
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25
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26
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27
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28
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29
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30
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31
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32
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33
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34
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35
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36
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37
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38
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39
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40
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41
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42
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43
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44
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45
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46
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47
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48
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49
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50
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51
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52
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53
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54
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55
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56
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57
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58
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59
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60
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61
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62
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63
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64
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65
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66
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67
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68
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69
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70
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71
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72
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73
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74
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75
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76
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77
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78
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79
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80
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81
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82
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83
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84
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85
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86
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87
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88
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89
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90
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91
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92
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93
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94
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95
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96
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97
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98
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99
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100
+ 620098,0,8750,timeout,0
101
+ 620099,0,8750,timeout,0
policies/stack_blocks/gpt6sol-s3/policy.py ADDED
@@ -0,0 +1,138 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Hand-written, deterministic block transport controller."""
2
+ import math
3
+ import numpy as np
4
+
5
+
6
+ class Policy:
7
+ def reset(self, obs, tools):
8
+ self.base = np.asarray(tools.hold_action(), dtype=np.float32)
9
+ self.phase = "approach"
10
+ self.age = 0
11
+ self.trip = 0
12
+
13
+ def change(self, name):
14
+ self.phase = name
15
+ self.age = 0
16
+
17
+ @staticmethod
18
+ def wrap(x):
19
+ return (x + math.pi) % (2 * math.pi) - math.pi
20
+
21
+ def go(self, a, q, x, y, yaw=0., speed=.27):
22
+ dx, dy = x - float(q[21]), y - float(q[22])
23
+ c, s = math.cos(float(q[24])), math.sin(float(q[24]))
24
+ a[0] = np.clip(1.5 * (c * dx + s * dy), -speed, speed)
25
+ a[1] = np.clip(1.5 * (-s * dx + c * dy), -speed, speed)
26
+ if abs(a[0]) < .06: a[0] = 0
27
+ if abs(a[1]) < .06: a[1] = 0
28
+ err = self.wrap(yaw - float(q[24]))
29
+ a[3] = np.clip(1.2 * err, -.5, .5)
30
+ return math.hypot(dx, dy) < .055 and abs(err) < .07
31
+
32
+ @staticmethod
33
+ def arms(a, p=-.5, torso=0., grip=1.):
34
+ a[4] = torso
35
+ a[5:12] = (p, .1, -.1, 0, 0, .3, -.6)
36
+ a[12:19] = (p, -.1, .1, 0, 0, .3, .6)
37
+ a[19:21] = grip
38
+
39
+ def act(self, obs, tools):
40
+ q = obs['low_dim_obs']
41
+ a = self.base.copy()
42
+ self.age += 1
43
+ p = self.phase
44
+ if p == 'approach':
45
+ self.arms(a, grip=0)
46
+ offset = (0., .10, -.10)[min(self.trip, 2)]
47
+ a[0] = .12 if q[21] < .22 else 0
48
+ a[1] = np.clip(1.5 * (offset - q[22]), -.12, .12)
49
+ if abs(a[1]) < .06: a[1] = 0
50
+ a[3] = np.clip(-1.2 * q[24], -.3, .3)
51
+ if q[21] >= .22 or self.age > 240:
52
+ self.change('reach')
53
+ elif p == 'reach':
54
+ self.arms(a, .1, .15, 0)
55
+ if self.age >= 80: self.change('close')
56
+ elif p == 'close':
57
+ self.arms(a, .1, .15, 1)
58
+ if self.age >= 60: self.change('lift1')
59
+ elif p == 'lift1':
60
+ z = min(1., self.age / 100.)
61
+ self.arms(a, .1 - .2*z, .15*(1-z), 1)
62
+ if self.age >= 100: self.change('lift2')
63
+ elif p == 'lift2':
64
+ z = min(1., self.age / 150.)
65
+ self.arms(a, -.1 - .4*z, 0, 1)
66
+ if self.age >= 150: self.change('back')
67
+ elif p == 'back':
68
+ self.arms(a)
69
+ if self.go(a, q, .02, 0, speed=.16) or self.age > 180:
70
+ self.change('north')
71
+ elif p == 'north':
72
+ self.arms(a)
73
+ if self.go(a, q, .02, 1.16) or self.age > 450:
74
+ self.change('east')
75
+ elif p == 'east':
76
+ self.arms(a)
77
+ if self.go(a, q, 1.45, 1.16) or self.age > 550:
78
+ self.change('turn_pad')
79
+ elif p == 'turn_pad':
80
+ self.arms(a)
81
+ if self.go(a, q, 1.45, 1.16, -math.pi/2, .18) or self.age > 260:
82
+ self.change('pad')
83
+ elif p == 'pad':
84
+ self.arms(a)
85
+ if self.go(a, q, 1.30, .77, -math.pi/2, .16) or self.age > 300:
86
+ self.change('lower_left')
87
+ elif p == 'lower_left':
88
+ z = min(1., self.age / 100.)
89
+ self.arms(a, -.5 + .6*z, .15*z)
90
+ a[12] = -.5
91
+ if self.age >= 100: self.change('release_left')
92
+ elif p == 'release_left':
93
+ self.arms(a, .1, .15)
94
+ a[12] = -.5
95
+ a[19] = 0
96
+ if self.age >= 75: self.change('raise_left')
97
+ elif p == 'raise_left':
98
+ self.arms(a, .1 - .6*min(1., self.age/100.))
99
+ a[19] = 0
100
+ if self.age >= 100: self.change('pad_right')
101
+ elif p == 'pad_right':
102
+ self.arms(a)
103
+ a[19] = 0
104
+ if self.go(a, q, 1.53, .77, -math.pi/2, .13) or self.age > 200:
105
+ self.change('lower_right')
106
+ elif p == 'lower_right':
107
+ z = min(1., self.age/100.)
108
+ self.arms(a)
109
+ a[19] = 0
110
+ a[4] = .15*z
111
+ a[12] = -.5 + .6*z
112
+ if self.age >= 100: self.change('release_right')
113
+ elif p == 'release_right':
114
+ self.arms(a)
115
+ a[19:21] = 0
116
+ a[4] = .15
117
+ a[12] = .1
118
+ if self.age >= 75: self.change('leave_pad')
119
+ elif p == 'leave_pad':
120
+ self.arms(a, grip=0)
121
+ if self.go(a, q, 1.45, 1.16, -math.pi/2, .2) or self.age > 350:
122
+ self.change('turn_home')
123
+ elif p == 'turn_home':
124
+ self.arms(a, grip=0)
125
+ if self.go(a, q, 1.45, 1.16, 0, .2) or self.age > 300:
126
+ self.change('west')
127
+ elif p == 'west':
128
+ self.arms(a, grip=0)
129
+ if self.go(a, q, .02, 1.16) or self.age > 600:
130
+ self.change('south')
131
+ elif p == 'south':
132
+ self.arms(a, grip=0)
133
+ if self.go(a, q, .02, 0) or self.age > 500:
134
+ self.trip += 1
135
+ self.change('approach' if self.trip < 3 else 'hold')
136
+ else:
137
+ self.arms(a, grip=0)
138
+ return a
policies/stack_blocks/kimi31pro-s1/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ seed,success,length,termination,fell
2
+ 620000,0,8750,timeout,0
3
+ 620001,0,8750,timeout,0
4
+ 620002,0,8750,timeout,0
5
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6
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7
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8
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9
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10
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11
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12
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13
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14
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15
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16
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17
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18
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19
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20
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21
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22
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23
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24
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25
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26
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27
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28
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29
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30
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31
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32
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33
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34
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35
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36
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37
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38
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39
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40
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41
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42
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43
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44
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45
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46
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50
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51
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52
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54
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55
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56
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57
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58
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59
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60
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61
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62
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63
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64
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65
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66
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67
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68
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69
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70
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71
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72
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73
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74
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75
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76
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77
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78
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79
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80
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81
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82
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83
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84
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85
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86
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87
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88
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89
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90
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91
+ 620089,0,8750,timeout,0
92
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93
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94
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95
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96
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97
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98
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99
+ 620097,0,8750,timeout,0
100
+ 620098,0,685,terminated,0
101
+ 620099,0,8750,timeout,0
policies/stack_blocks/kimi31pro-s1/policy.py ADDED
@@ -0,0 +1,379 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Deterministic hand-written controller for the three-block stacking task."""
2
+
3
+ import cv2
4
+ import numpy as np
5
+
6
+
7
+ class Policy:
8
+ def reset(self, obs, tools):
9
+ self.hold = np.asarray(tools.hold_action(), dtype=np.float32)
10
+ self.hold[19:21] = 0.0
11
+ self.action = self.hold.copy()
12
+ self.start_action = self.action.copy()
13
+ self.trip = 0
14
+ self.phase = "pre_grasp"
15
+ self.phase_start = int(obs["t"])
16
+ self.torso_pitch = 0.0
17
+ self.hold_ref = None
18
+ self.prev = None
19
+ self.stable = 0
20
+ self.lost = 0
21
+ self.acquire_attempts = 0
22
+ self.yaw_adjust = 0.0
23
+ self.need_visual = False
24
+ self.retries = [0, 0, 0]
25
+ blocks = self._components(tools.image("head"), "red", 15)
26
+ blocks = sorted(blocks, key=lambda c: c[0][0])
27
+ if len(blocks) >= 3:
28
+ self.initial_outer = blocks[1][0][0] > 40
29
+ selected = blocks[0] if self.initial_outer else blocks[1]
30
+ else:
31
+ self.initial_outer = False
32
+ selected = blocks[len(blocks) // 2] if blocks else (np.array([36.0, 25.0]), 40)
33
+ self.initial_target = selected[0].copy()
34
+ cx = blocks[1][0][0] if len(blocks) >= 3 else 36.0
35
+ self.initial_yaw = 0.3 if self.initial_outer else float(np.clip(-0.3 - (cx - 36) * 0.0167, -0.55, 0.1))
36
+ self.initial_pitch = -0.1 if self.initial_outer else -0.2
37
+
38
+ @staticmethod
39
+ def _components(im, color, min_area=1):
40
+ im = np.asarray(im)
41
+ if color == "green":
42
+ mask = ((im[:, :, 1] > 120) & (im[:, :, 1] > 1.25 * im[:, :, 0])
43
+ & (im[:, :, 1] > 1.15 * im[:, :, 2]))
44
+ else:
45
+ mask = ((im[:, :, 0] > 150) & (im[:, :, 0] > 1.5 * im[:, :, 1])
46
+ & (im[:, :, 0] > 1.5 * im[:, :, 2]))
47
+ n, _, stats, cents = cv2.connectedComponentsWithStats(mask.astype(np.uint8))
48
+ return [(np.asarray(cents[i]), int(stats[i, 4])) for i in range(1, n)
49
+ if stats[i, 4] >= min_area]
50
+
51
+ def _go(self, name, obs):
52
+ self.phase = name
53
+ self.phase_start = int(obs["t"])
54
+ self.start_action = self.action.copy()
55
+ self.stable = 0
56
+ self.lost = 0
57
+ self.prev = None
58
+ if name == "pre_grasp":
59
+ self.yaw_adjust = 0.0
60
+ if name in ("sidestep", "turn_home"):
61
+ self.hold_ref = None
62
+ self.torso_pitch = 0.0
63
+ if name == "acquire":
64
+ self.torso_pitch = -0.2
65
+ self.acquire_attempts += 1
66
+ if name == "approach_settle" and self.trip == 2:
67
+ pel = obs["low_dim_obs"][21:25]
68
+ self.hold_ref = np.array([pel[0], pel[1], pel[3]])
69
+ if name == "place_base":
70
+ pel = obs["low_dim_obs"][21:25]
71
+ self.hold_ref = np.array([pel[0], pel[1], pel[3]])
72
+ if name == "turn_home":
73
+ self.torso_pitch = 0.0
74
+ print("phase", self.trip, name, "t", int(obs["t"]),
75
+ "pel", np.round(obs["low_dim_obs"][21:25], 3), flush=True)
76
+
77
+ def _pose(self, p=-0.2, yaw=0.0, elbow=0.3, grip=0.0, height=0.74):
78
+ raw = self.hold.copy()
79
+ raw[2] = height
80
+ raw[4] = self.torso_pitch
81
+ raw[5:12] = [p, 0.0, yaw, elbow, 0.0, 0.0, 0.0]
82
+ raw[12:19] = 0.0
83
+ raw[19] = grip
84
+ raw[20] = 0.0
85
+ return raw
86
+
87
+ def _carry(self, grip=1.0):
88
+ return self._pose(-0.55, -0.3, 0.2, grip, 0.8)
89
+
90
+ def _grasp_pose(self, grip=0.0):
91
+ if self.trip == 1:
92
+ yaw = 0.15 + 0.1 * min(self.retries[1], 2) + self.yaw_adjust
93
+ return self._pose(-0.2, yaw, 0.3, grip, 0.74)
94
+ if self.trip == 2:
95
+ elbow = max(0.3, 0.4 - 0.05 * self.retries[2])
96
+ return self._pose(-0.2, -0.3 + self.yaw_adjust, elbow, grip, 0.74)
97
+ return self._pose(self.initial_pitch, self.initial_yaw, 0.3, grip, 0.74)
98
+
99
+ def _place_pose(self, grip=1.0):
100
+ # Later blocks are released progressively higher above the same pad.
101
+ p = (-0.2, -0.32, -0.44)[self.trip]
102
+ h = (0.74, 0.76, 0.78)[self.trip]
103
+ return self._pose(p, -0.3, 0.3, grip, h)
104
+
105
+ def _apply_base_hold(self, obs, raw):
106
+ if self.hold_ref is None:
107
+ return raw
108
+ pel = obs["low_dim_obs"][21:25]
109
+ yaw = pel[3]
110
+ dx, dy = self.hold_ref[0] - pel[0], self.hold_ref[1] - pel[1]
111
+ ex = np.cos(yaw) * dx + np.sin(yaw) * dy
112
+ ey = -np.sin(yaw) * dx + np.cos(yaw) * dy
113
+ raw[0] = 0 if abs(ex) < 0.015 else np.clip(2 * ex, -0.15, 0.15)
114
+ raw[1] = 0 if abs(ey) < 0.015 else np.clip(2 * ey, -0.15, 0.15)
115
+ if 0 < abs(raw[0]) < 0.06:
116
+ raw[0] = np.sign(raw[0]) * 0.06
117
+ if 0 < abs(raw[1]) < 0.06:
118
+ raw[1] = np.sign(raw[1]) * 0.06
119
+ ye = (self.hold_ref[2] - yaw + np.pi) % (2 * np.pi) - np.pi
120
+ raw[3] = 0 if abs(ye) < 0.02 else np.clip(ye, -0.3, 0.3)
121
+ return raw
122
+
123
+ def _interp(self, target, pt, duration):
124
+ alpha = min(1.0, (pt + 1) / float(duration))
125
+ raw = self.start_action * (1 - alpha) + target * alpha
126
+ raw[0] = target[0]
127
+ raw[1] = target[1]
128
+ raw[3] = target[3]
129
+ return raw
130
+
131
+ def act(self, obs, tools):
132
+ pt = int(obs["t"]) - self.phase_start
133
+ name = self.phase
134
+ raw = self.action.copy()
135
+
136
+ if name == "pre_grasp":
137
+ raw = self._interp(self._pose(-0.2, 0, 0.3, 0, 0.74), pt, 40)
138
+ if pt >= 39:
139
+ self._go("visual_align" if self.need_visual or self.trip >= 1 else "approach", obs)
140
+
141
+ elif name == "visual_align":
142
+ cs = self._components(tools.image("head"), "red", 15)
143
+ if self.prev is None:
144
+ if cs:
145
+ if self.trip == 0:
146
+ self.prev = min(cs, key=lambda c: abs(c[0][0] - self.initial_target[0]))[0]
147
+ else:
148
+ self.prev = (min if self.trip == 1 else max)(cs, key=lambda c: c[0][0])[0]
149
+ else:
150
+ self.prev = self.initial_target.copy() if self.trip == 0 else (np.array([13.0, 24.0]) if self.trip == 1 else np.array([67.0, 24.0]))
151
+ if cs:
152
+ c = min(cs, key=lambda c: np.linalg.norm(c[0] - self.prev))
153
+ if np.linalg.norm(c[0] - self.prev) < 20:
154
+ self.prev = c[0]
155
+ target_x = self.initial_target[0] if self.trip == 0 else (13 if self.trip == 1 else 36)
156
+ target_y = self.initial_target[1] if self.trip == 0 else 25
157
+ ex, ey = target_x - self.prev[0], target_y - self.prev[1]
158
+ self.stable = self.stable + 1 if abs(ex) < 2.5 and abs(ey) < 2.5 else 0
159
+ raw = self._pose(-0.2, 0, 0.3, 0, 0.74)
160
+ raw[0] = 0 if abs(ey) < 1.2 else np.sign(ey) * min(0.16, max(0.06, abs(ey) * 0.025))
161
+ raw[1] = 0 if abs(ex) < 1.2 else np.sign(ex) * min(0.3, max(0.06, abs(ex) * 0.02))
162
+ raw[3] = np.clip(-0.5 * obs["low_dim_obs"][24], -0.25, 0.25)
163
+ if self.stable >= 10 or pt >= 239:
164
+ self._go("visual_settle", obs)
165
+
166
+ elif name == "visual_settle":
167
+ raw = self._pose(-0.2, 0, 0.3, 0, 0.74)
168
+ if pt >= 19:
169
+ self._go("approach", obs)
170
+
171
+ elif name == "approach":
172
+ raw = self._pose(-0.2, 0, 0.3, 0, 0.74)
173
+ raw[0] = 0.2
174
+ if pt >= 29:
175
+ self._go("approach_settle", obs)
176
+
177
+ elif name == "approach_settle":
178
+ raw = self._pose(-0.2, 0, 0.3, 0, 0.74)
179
+ raw = self._apply_base_hold(obs, raw)
180
+ if pt >= 19:
181
+ self._go("align", obs)
182
+
183
+ elif name == "align":
184
+ raw = self._interp(self._grasp_pose(0), pt, 30)
185
+ raw = self._apply_base_hold(obs, raw)
186
+ if pt >= 29:
187
+ self._go("settle", obs)
188
+
189
+ elif name == "settle":
190
+ raw = self._grasp_pose(0)
191
+ raw = self._apply_base_hold(obs, raw)
192
+ if pt >= 19:
193
+ self._go("close", obs)
194
+
195
+ elif name == "close":
196
+ if pt == 0 and self.trip >= 1:
197
+ cs = self._components(tools.image("left_wrist"), "red", 30)
198
+ if cs:
199
+ c = max(cs, key=lambda c: c[1])[0]
200
+ self.yaw_adjust = float(np.clip(0.008 * (40 - c[0]), -0.15, 0.28))
201
+ else:
202
+ self.yaw_adjust = 0.2 if self.trip == 1 else 0.15
203
+ raw = self._interp(self._grasp_pose(1), pt, 30)
204
+ raw = self._apply_base_hold(obs, raw)
205
+ if pt >= 29:
206
+ self._go("close_settle", obs)
207
+
208
+ elif name == "close_settle":
209
+ raw = self._grasp_pose(1)
210
+ raw = self._apply_base_hold(obs, raw)
211
+ if pt >= 19:
212
+ self._go("lift", obs)
213
+
214
+ elif name == "lift":
215
+ raw = self._interp(self._carry(1), pt, 50)
216
+ raw = self._apply_base_hold(obs, raw)
217
+ if pt >= 49:
218
+ self._go("lift_settle", obs)
219
+
220
+ elif name == "lift_settle":
221
+ raw = self._carry(1)
222
+ raw = self._apply_base_hold(obs, raw)
223
+ if pt >= 29:
224
+ if np.mean(obs["low_dim_obs"][10:12]) < -0.017:
225
+ self.retries[self.trip] += 1
226
+ self.need_visual = True
227
+ print("retry empty grasp", self.trip, self.retries[self.trip], flush=True)
228
+ self._go("pre_grasp", obs)
229
+ else:
230
+ self._go("sidestep", obs)
231
+
232
+ elif name == "sidestep":
233
+ raw = self._carry(1)
234
+ pel = obs["low_dim_obs"][21:25]
235
+ raw[1] = 0.5
236
+ raw[3] = np.clip(-pel[3], -0.3, 0.3)
237
+ if pel[1] >= 0.82 or pt >= 299:
238
+ self._go("side_settle", obs)
239
+
240
+ elif name == "side_settle":
241
+ raw = self._carry(1)
242
+ if pt >= 19:
243
+ self._go("pad_forward", obs)
244
+
245
+ elif name == "pad_forward":
246
+ raw = self._carry(1)
247
+ pel = obs["low_dim_obs"][21:25]
248
+ raw[0] = 0.35
249
+ raw[1] = np.clip(2 * (0.82 - pel[1]), -0.15, 0.15)
250
+ raw[3] = np.clip(-pel[3], -0.3, 0.3)
251
+ if pel[0] > 1.3 or pt >= 399:
252
+ self._go("acquire", obs)
253
+
254
+ elif name == "acquire":
255
+ head_green = self._components(tools.image("head"), "green")
256
+ raw = self._carry(1)
257
+ if head_green:
258
+ self._go("green_servo", obs)
259
+ else:
260
+ cs = self._components(tools.image("right_wrist"), "green")
261
+ if cs:
262
+ c = max(cs, key=lambda c: c[1])[0]
263
+ raw[0] = 0.08 if c[1] < 18 else 0
264
+ raw[3] = np.clip(-0.015 * (c[0] - 42), -0.3, 0.3)
265
+ else:
266
+ raw[3] = -0.5
267
+ if pt >= 399:
268
+ self._go("green_servo", obs)
269
+
270
+ elif name == "green_servo":
271
+ cs = self._components(tools.image("head"), "green")
272
+ self.lost = 0 if cs else self.lost + 1
273
+ if cs:
274
+ self.prev = max(cs, key=lambda c: c[1])[0]
275
+ ex, ey = 36 - self.prev[0], 25 - self.prev[1]
276
+ elif self.prev is not None:
277
+ ex, ey = 36 - self.prev[0], 25 - self.prev[1]
278
+ else:
279
+ ex, ey = 0, 3
280
+ self.stable = self.stable + 1 if cs and abs(ex) < 6 and abs(ey) < 12 else 0
281
+ raw = self._carry(1)
282
+ if cs:
283
+ raw[0] = 0 if abs(ey) < 1.5 or abs(ex) > 8 else np.sign(ey) * min(0.18, max(0.06, abs(ey) * 0.025))
284
+ raw[3] = np.clip(0.02 * ex, -0.3, 0.3)
285
+ else:
286
+ raw[0] = 0.06 if self.lost < 80 and self.prev is not None and self.prev[1] < 8 else 0
287
+ raw[3] = np.clip(0.02 * ex, -0.15, 0.15) if self.prev is not None else -0.08
288
+ if self.stable >= 20 or pt >= 179 or (self.lost >= 80 and self.prev is not None):
289
+ self._go("place_base", obs)
290
+ elif pt >= 599:
291
+ self._go("acquire", obs)
292
+
293
+ elif name == "place_base":
294
+ raw = self._place_pose(1)
295
+ raw = self._apply_base_hold(obs, raw)
296
+ if pt >= 19:
297
+ self._go("lower", obs)
298
+
299
+ elif name == "lower":
300
+ raw = self._interp(self._place_pose(1), pt, 30)
301
+ raw = self._apply_base_hold(obs, raw)
302
+ if pt >= 29:
303
+ self._go("place_settle", obs)
304
+
305
+ elif name == "place_settle":
306
+ raw = self._place_pose(1)
307
+ raw = self._apply_base_hold(obs, raw)
308
+ if pt >= 19:
309
+ self._go("release", obs)
310
+
311
+ elif name == "release":
312
+ raw = self._interp(self._place_pose(0), pt, 30)
313
+ raw = self._apply_base_hold(obs, raw)
314
+ if pt >= 29:
315
+ self._go("release_settle", obs)
316
+
317
+ elif name == "release_settle":
318
+ raw = self._place_pose(0)
319
+ raw = self._apply_base_hold(obs, raw)
320
+ if pt >= 29:
321
+ self._go("retreat", obs)
322
+
323
+ elif name == "retreat":
324
+ raw = self._interp(self._carry(0), pt, 50)
325
+ raw = self._apply_base_hold(obs, raw)
326
+ if pt >= 49:
327
+ self._go("retreat_settle", obs)
328
+
329
+ elif name == "retreat_settle":
330
+ raw = self._carry(0)
331
+ raw = self._apply_base_hold(obs, raw)
332
+ if pt >= 29:
333
+ if self.trip < 2:
334
+ self._go("turn_home", obs)
335
+ else:
336
+ self._go("success_wait", obs)
337
+
338
+ elif name == "turn_home":
339
+ raw = self._carry(0)
340
+ yaw = obs["low_dim_obs"][24]
341
+ err = (-yaw + np.pi) % (2 * np.pi) - np.pi
342
+ raw[3] = np.clip(1.5 * err, -0.8, 0.8)
343
+ if abs(err) < 0.035 or pt >= 399:
344
+ self._go("home_settle", obs)
345
+
346
+ elif name == "home_settle":
347
+ raw = self._carry(0)
348
+ if pt >= 19:
349
+ self._go("back_source", obs)
350
+
351
+ elif name == "back_source":
352
+ raw = self._carry(0)
353
+ pel = obs["low_dim_obs"][21:25]
354
+ raw[0] = -0.5
355
+ raw[3] = np.clip(-pel[3], -0.3, 0.3)
356
+ if pel[0] <= 0.17 or pt >= 499:
357
+ self._go("source_lateral", obs)
358
+
359
+ elif name == "source_lateral":
360
+ raw = self._carry(0)
361
+ pel = obs["low_dim_obs"][21:25]
362
+ raw[1] = np.clip(-3 * pel[1], -0.4, 0.4)
363
+ if 0 < abs(raw[1]) < 0.06:
364
+ raw[1] = np.sign(raw[1]) * 0.06
365
+ raw[3] = np.clip(-pel[3], -0.3, 0.3)
366
+ if abs(pel[1]) < 0.05 or pt >= 299:
367
+ self._go("source_settle", obs)
368
+
369
+ elif name == "source_settle":
370
+ raw = self._carry(0)
371
+ if pt >= 19:
372
+ self.trip += 1
373
+ self._go("pre_grasp", obs)
374
+
375
+ elif name == "success_wait":
376
+ raw = self._carry(0)
377
+
378
+ self.action = np.asarray(raw, dtype=np.float32)
379
+ return self.action
policies/stack_blocks/kimi31pro-s2/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
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92
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93
+ 620091,0,8750,timeout,0
94
+ 620092,0,2201,terminated,0
95
+ 620093,0,8750,timeout,0
96
+ 620094,0,8750,timeout,0
97
+ 620095,0,8750,timeout,0
98
+ 620096,0,8750,timeout,0
99
+ 620097,0,8750,timeout,0
100
+ 620098,0,8750,timeout,0
101
+ 620099,0,8750,timeout,0
policies/stack_blocks/kimi31pro-s2/policy.py ADDED
@@ -0,0 +1,243 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Deterministic hand-written controller for the G1 block-stacking task.
2
+
3
+ The robot carries one block at a time. It uses proprioceptive waypoint control
4
+ in the open lane beside the tables and small, fixed arm motions for grasping
5
+ and releasing. Camera measurements are used only to check/adjust a grasp.
6
+ """
7
+
8
+ import cv2
9
+ import numpy as np
10
+
11
+
12
+ class Policy:
13
+ def reset(self, obs, tools):
14
+ self.a = np.asarray(tools.hold_action(), dtype=np.float32).copy()
15
+ self.cycle = 0
16
+ self.pick_y = 0.0
17
+ self.retries = 0
18
+ self.phase = "initial_walk"
19
+ self.n = 0
20
+ self.goal = np.zeros(3)
21
+ self.next_phase = ""
22
+ self.duration = 1
23
+ self.ramp_start = self.a.copy()
24
+ self.ramp_target = self.a.copy()
25
+ self.initial_x = float(obs["low_dim_obs"][21])
26
+ self.walk_goal = self.initial_x + 0.065
27
+
28
+ def _start(self, name, tools):
29
+ self.phase = name
30
+ self.n = 0
31
+ if name == "approach":
32
+ self.a[0:2] = 0
33
+ self.a[3] = 0
34
+ self.a[19:21] = 0
35
+ target = self.a.copy()
36
+ target[2] = 0.60
37
+ target[5] = target[12] = -0.35
38
+ target[6] = 0
39
+ target[7] = 0
40
+ target[10] = 0
41
+ target[13] = -0.45
42
+ target[14] = 0
43
+ target[17] = 0
44
+ self._ramp(target, "wrist", 75)
45
+ elif name == "wrist":
46
+ target = self.a.copy()
47
+ target[10] = 0.22
48
+ target[17] = 0.12
49
+ self._ramp(target, "pick_adjust", 45)
50
+ elif name == "lift":
51
+ target = self.a.copy()
52
+ target[2] = 0.74
53
+ self._ramp(target, "verify", 90)
54
+ elif name == "retract":
55
+ target = self.a.copy()
56
+ target[12] = 0
57
+ target[13] = 0
58
+ target[14] = 0
59
+ target[17] = 0
60
+ self._ramp(target, "side", 50)
61
+ elif name == "side":
62
+ self._go(-0.05, 0.90, 0.0, "far", 700)
63
+ elif name == "far":
64
+ self._go(1.63, 1.0, 0.0, "turn_dest", 800)
65
+ elif name == "turn_dest":
66
+ self._turn(-np.pi / 2, "preextend")
67
+ elif name == "preextend":
68
+ target = self.a.copy()
69
+ target[5] = -0.40
70
+ target[6] = 0
71
+ target[7] = 0
72
+ self._ramp(target, "dest", 60)
73
+ elif name == "dest":
74
+ self._go(1.63, 0.64, -np.pi / 2, "deepplace", 180)
75
+ elif name == "deepplace":
76
+ target = self.a.copy()
77
+ target[2] = 0.82
78
+ target[5] = -0.70
79
+ target[7] = -0.10
80
+ self._ramp(target, "release", 70)
81
+ elif name == "retreat":
82
+ target = self.a.copy()
83
+ target[2] = 0.74
84
+ target[5] = 0
85
+ target[6] = 0
86
+ target[7] = 0
87
+ target[10] = 0
88
+ self._ramp(target, "after_place", 80)
89
+ elif name == "return_corridor":
90
+ self._go(1.63, 0.90, -np.pi / 2, "return_turn", 400)
91
+ elif name == "return_turn":
92
+ self._turn(0.0, "return_side")
93
+ elif name == "return_side":
94
+ self._go(-0.05, 0.90, 0.0, "return_lateral", 800)
95
+ elif name == "return_lateral":
96
+ self._go(-0.05, self.pick_y, 0.0, "source_x", 500)
97
+ elif name == "source_x":
98
+ self._go(0.175, self.pick_y, 0.0, "approach", 400)
99
+ elif name == "finish":
100
+ self.a[0:2] = 0
101
+ self.a[3] = 0
102
+ self.a[19:21] = 0
103
+
104
+ def _ramp(self, target, next_phase, duration):
105
+ self.ramp_start = self.a.copy()
106
+ self.ramp_target = np.asarray(target, dtype=np.float32).copy()
107
+ self.duration = int(duration)
108
+ self.next_phase = next_phase
109
+ self.phase = "ramp"
110
+ self.n = 0
111
+
112
+ def _go(self, x, y, yaw, next_phase, limit):
113
+ self.goal = np.array([x, y, yaw], dtype=float)
114
+ self.next_phase = next_phase
115
+ self.duration = int(limit)
116
+ self.phase = "go"
117
+ self.n = 0
118
+
119
+ def _turn(self, yaw, next_phase):
120
+ self.goal = np.array([yaw], dtype=float)
121
+ self.next_phase = next_phase
122
+ self.phase = "turn"
123
+ self.n = 0
124
+
125
+ @staticmethod
126
+ def _red_centroid(image):
127
+ r = image[:, :, 0].astype(np.float32)
128
+ g = image[:, :, 1].astype(np.float32)
129
+ b = image[:, :, 2].astype(np.float32)
130
+ mask = ((r > 110) & (r > 1.5 * g) & (r > 1.5 * b)).astype(np.uint8)
131
+ count, _, stats, cents = cv2.connectedComponentsWithStats(mask)
132
+ if count <= 1:
133
+ return None
134
+ j = 1 + int(np.argmax(stats[1:, cv2.CC_STAT_AREA]))
135
+ if stats[j, cv2.CC_STAT_AREA] < 20:
136
+ return None
137
+ return cents[j]
138
+
139
+ def act(self, obs, tools):
140
+ self.n += 1
141
+ p = obs["low_dim_obs"][21:25]
142
+
143
+ if self.phase == "initial_walk":
144
+ err = self.walk_goal - p[0]
145
+ self.a[0] = np.sign(err) * max(0.06, min(0.12, 2 * abs(err))) if abs(err) > 0.006 else 0
146
+ self.a[1] = np.clip(-p[1], -0.06, 0.06) if abs(p[1]) > 0.008 else 0
147
+ self.a[3] = np.clip(-p[3], -0.2, 0.2)
148
+ if self.n >= 170:
149
+ self.a[0:2] = 0
150
+ self.a[3] = 0
151
+ self._start("approach", tools)
152
+
153
+ elif self.phase == "ramp":
154
+ # The grasp timings deliberately match the calibrated contact motion.
155
+ den = 35 if self.duration == 45 else 70 if self.duration == 90 else self.duration * 0.8
156
+ u = min(1.0, self.n / den)
157
+ self.a = self.ramp_start * (1 - u) + self.ramp_target * u
158
+ self.a[0:2] = 0
159
+ self.a[3] = 0
160
+ if self.n >= self.duration:
161
+ self._start(self.next_phase, tools)
162
+
163
+ elif self.phase == "pick_adjust":
164
+ if self.cycle == 0 and self.retries == 0:
165
+ self._start("close", tools)
166
+ return np.asarray(self.a, dtype=np.float32)
167
+ image = tools.image("left_wrist")
168
+ center = self._red_centroid(image)
169
+ if center is not None:
170
+ dx, dy = float(center[0]) - 44.0, float(center[1]) - 64.0
171
+ if abs(dx) > 6 or abs(dy) > 9:
172
+ target = self.a.copy()
173
+ target[6] = np.clip(target[6] + np.clip(0.005 * dx, -0.08, 0.08), -0.15, 0.15)
174
+ target[10] = np.clip(target[10] + np.clip(0.01 * dy, -0.10, 0.10), 0.05, 0.42)
175
+ self._ramp(target, "close", 20)
176
+ else:
177
+ self._start("close", tools)
178
+ else:
179
+ self._start("close", tools)
180
+
181
+ elif self.phase == "close":
182
+ self.a[19:21] = min(1.0, self.n / 60.0)
183
+ if self.n >= 80:
184
+ self._start("lift", tools)
185
+
186
+ elif self.phase == "verify":
187
+ fingers = obs["low_dim_obs"][10:12]
188
+ held = float(np.mean(fingers)) > -0.017
189
+ if not held and self.retries < 1:
190
+ self.retries += 1
191
+ self.pick_y += 0.05
192
+ self.a[19:21] = 0
193
+ self._start("source_x", tools)
194
+ else:
195
+ self._start("retract", tools)
196
+
197
+ elif self.phase == "go":
198
+ dp = self.goal[:2] - p[:2]
199
+ dist = float(np.linalg.norm(dp))
200
+ ye = (self.goal[2] - p[3] + np.pi) % (2 * np.pi) - np.pi
201
+ if dist < 0.05 or self.n >= self.duration:
202
+ self.a[0:2] = 0
203
+ self.a[3] = 0
204
+ self._start(self.next_phase, tools)
205
+ else:
206
+ speed_cap = 0.30 if self.next_phase == "deepplace" else 0.34
207
+ speed = max(0.065, min(speed_cap, 0.65 * dist))
208
+ vxw, vyw = dp / dist * speed
209
+ c, s = np.cos(p[3]), np.sin(p[3])
210
+ self.a[0] = c * vxw + s * vyw
211
+ self.a[1] = -s * vxw + c * vyw
212
+ self.a[3] = np.clip(1.2 * ye, -0.15, 0.15)
213
+
214
+ elif self.phase == "turn":
215
+ ye = (self.goal[0] - p[3] + np.pi) % (2 * np.pi) - np.pi
216
+ self.a[0:2] = 0
217
+ self.a[3] = np.clip(1.2 * ye, -0.35, 0.35)
218
+ if abs(ye) < 0.03 or self.n >= 420:
219
+ self._start(self.next_phase, tools)
220
+
221
+ elif self.phase == "release":
222
+ self.a[19] = max(0.0, 1.0 - self.n / 40.0)
223
+ if self.n >= 55:
224
+ self._start("retreat", tools)
225
+
226
+ elif self.phase == "after_place":
227
+ if self.cycle >= 2:
228
+ self._start("finish", tools)
229
+ else:
230
+ self.cycle += 1
231
+ self.pick_y = (0.0, -0.15, -0.35)[self.cycle]
232
+ self.retries = 0
233
+ self._start("return_corridor", tools)
234
+
235
+ elif self.phase == "finish":
236
+ self.a[0:2] = 0
237
+ self.a[3] = 0
238
+ self.a[19:21] = 0
239
+
240
+ if obs.get("fell", False):
241
+ self.a[0:2] = 0
242
+ self.a[3] = 0
243
+ return np.asarray(self.a, dtype=np.float32)
policies/stack_blocks/kimi31pro-s3/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ seed,success,length,termination,fell
2
+ 620000,0,8750,timeout,0
3
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4
+ 620002,0,8750,timeout,0
5
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6
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7
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8
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9
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10
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11
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19
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policies/stack_blocks/kimi31pro-s3/policy.py ADDED
@@ -0,0 +1,247 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Deterministic hand-written controller for the G1 block-stacking task."""
2
+
3
+ import cv2
4
+ import numpy as np
5
+
6
+
7
+ class Policy:
8
+ PICK = np.array([-.40, 0., 0., .75, 0., -.30, 0.] + [0.] * 7)
9
+ CARRY = np.array([-.85, 0., 0., 1.20, 0., -.30, 0.] + [0.] * 7)
10
+
11
+ def reset(self, obs, tools):
12
+ self.hold = np.asarray(tools.hold_action(), dtype=np.float32)
13
+ self.arms = np.zeros(14, dtype=float)
14
+ self.arm_start = self.arms.copy()
15
+ self.arm_goal = self.arms.copy()
16
+ self.grip_l = 0.
17
+ self.height = .74
18
+ self.pitch = 0.
19
+ self.tx = .15
20
+ self.ty = 0.
21
+ self.tyaw = 0.
22
+ self.speed = .18
23
+ self.phase = "aim"
24
+ self.k = 0
25
+ self.stable = 0
26
+ self.placed = 0
27
+ self.retries = 0
28
+ self.roll_goal = None
29
+ self._aim(obs, tools)
30
+
31
+ def _go(self, phase):
32
+ self.phase = phase
33
+ self.k = 0
34
+ self.stable = 0
35
+
36
+ def _aim(self, obs, tools):
37
+ """Choose the leftmost visible source cube and stand off from it."""
38
+ im = tools.image("head")
39
+ r = im[:, :, 0].astype(np.float32)
40
+ g = im[:, :, 1].astype(np.float32)
41
+ b = im[:, :, 2].astype(np.float32)
42
+ mask = ((r > 150) & (r > 1.7 * g) & (r > 1.7 * b)).astype(np.uint8)
43
+ n, labels, stats, centers = cv2.connectedComponentsWithStats(mask, 8)
44
+ candidates = []
45
+ for i in range(1, n):
46
+ area = int(stats[i, cv2.CC_STAT_AREA])
47
+ cx, cy = centers[i]
48
+ if area >= 10 and cy < 65:
49
+ candidates.append((float(cx), float(cy), area))
50
+ if candidates:
51
+ cx, cy, _ = min(candidates, key=lambda v: v[0])
52
+ self.tx = float(np.clip(.15 + .008 * (22. - cy), .06, .28))
53
+ self.ty = float(np.clip(.008 * (17. - cx), -.38, .22))
54
+ else:
55
+ self.tx, self.ty = .15, 0.
56
+ self.tyaw = 0.
57
+ self.speed = .18
58
+
59
+ def _arm_move(self, goal, duration, next_phase):
60
+ if self.k == 0:
61
+ self.arm_start = self.arms.copy()
62
+ self.arm_goal = np.asarray(goal, dtype=float)
63
+ u = min(1., (self.k + 1) / float(duration))
64
+ self.arms = self.arm_start + u * (self.arm_goal - self.arm_start)
65
+ if self.k + 1 >= duration:
66
+ self.arms = self.arm_goal.copy()
67
+ self._go(next_phase)
68
+
69
+ def _goto(self, next_phase, max_steps, tolerance=.055):
70
+ q = self.low
71
+ yaw_err = (self.tyaw - float(q[24]) + np.pi) % (2 * np.pi) - np.pi
72
+ if (abs(self.tx - q[21]) < tolerance and
73
+ abs(self.ty - q[22]) < tolerance and abs(yaw_err) < .08):
74
+ self.stable += 1
75
+ else:
76
+ self.stable = 0
77
+ if self.stable > 12 or self.k + 1 >= max_steps:
78
+ self._go(next_phase)
79
+
80
+ def _base_action(self):
81
+ q = self.low
82
+ x, y, yaw = float(q[21]), float(q[22]), float(q[24])
83
+ err = (self.tyaw - yaw + np.pi) % (2 * np.pi) - np.pi
84
+ ex, ey = self.tx - x, self.ty - y
85
+ c, s = np.cos(yaw), np.sin(yaw)
86
+ bx, by = c * ex + s * ey, -s * ex + c * ey
87
+ a = self.hold.copy()
88
+ a[0] = np.clip(2.5 * bx, -self.speed, self.speed) if abs(bx) > .012 else 0.
89
+ a[1] = np.clip(2.5 * by, -self.speed, self.speed) if abs(by) > .015 else 0.
90
+ if 0 < abs(a[0]) < .06:
91
+ a[0] = np.sign(a[0]) * .06
92
+ if 0 < abs(a[1]) < .06:
93
+ a[1] = np.sign(a[1]) * .06
94
+ a[2] = self.height
95
+ a[3] = np.clip(1.4 * err, -.38, .38) if abs(err) > .04 else 0.
96
+ a[4] = self.pitch
97
+ if abs(err) > .25:
98
+ a[0] = a[1] = 0.
99
+ a[5:19] = self.arms
100
+ a[19] = self.grip_l
101
+ a[20] = 0.
102
+ return a
103
+
104
+ def act(self, obs, tools):
105
+ self.low = np.asarray(obs["low_dim_obs"])
106
+ phase = self.phase
107
+ self.grip_l = 0. if phase in ("aim", "reach", "home") else self.grip_l
108
+
109
+ if phase == "aim":
110
+ self.arms = np.zeros(14)
111
+ self.grip_l = 0.
112
+ self.speed = .18
113
+ if self.k + 1 >= 15:
114
+ self._go("reach")
115
+
116
+ elif phase == "home":
117
+ self.grip_l = 0.
118
+ self.speed = .30
119
+ self._arm_move(np.zeros(14), 50, "aim")
120
+ if self.phase == "aim":
121
+ self._aim(obs, tools)
122
+
123
+ elif phase == "reach":
124
+ self.grip_l = 0.
125
+ self._arm_move(self.PICK, 100, "rolls")
126
+
127
+ elif phase == "rolls":
128
+ k = self.k
129
+ if k < 300:
130
+ cycle = k // 100
131
+ local = k % 100
132
+ self.arms[4] = (-1.57, -1., -.5)[cycle]
133
+ self.grip_l = 0. if local < 40 or local >= 80 else 1.
134
+ else:
135
+ self.arms[4] = 0.
136
+ self.grip_l = 0. if k < 340 else 1.
137
+ if k + 1 >= 380:
138
+ self._go("check_grasp")
139
+
140
+ elif phase == "check_grasp":
141
+ self.arms[4] = 0.
142
+ self.grip_l = 1.
143
+ fingers = self.low[[10, 11]]
144
+ if np.mean(fingers) < -.017 and self.retries < 1:
145
+ self.retries += 1
146
+ self.tx = float(np.clip(self.tx + .025, .06, .29))
147
+ self.ty = float(np.clip(self.ty - .02, -.40, .24))
148
+ self._go("rolls")
149
+ else:
150
+ self.retries = 0
151
+ self._go("lift")
152
+
153
+ elif phase == "lift":
154
+ self.grip_l = 1.
155
+ self.height = .74 + .10 * min(1., (self.k + 1) / 60.)
156
+ if self.k + 1 >= 60:
157
+ self._go("back")
158
+
159
+ elif phase == "back":
160
+ self.grip_l = 1.
161
+ self.height = .84
162
+ self.tx = .05
163
+ self.speed = .22
164
+ self._goto("lane", 180)
165
+
166
+ elif phase == "lane":
167
+ self.grip_l = 1.
168
+ self.height = .78
169
+ self.speed = .30
170
+ self.tx, self.ty, self.tyaw = .05, 1.0, 0.
171
+ self._goto("destination_x", 300)
172
+
173
+ elif phase == "destination_x":
174
+ self.speed = .30
175
+ self.tx, self.ty, self.tyaw = 1.55, 1.0, 0.
176
+ self._goto("turn_destination", 450)
177
+
178
+ elif phase == "turn_destination":
179
+ self.speed = .20
180
+ self.tx, self.ty, self.tyaw = 1.55, 1.0, -1.57
181
+ self._goto("raise_carry", 250)
182
+
183
+ elif phase == "raise_carry":
184
+ self.speed = .18
185
+ self._arm_move(self.CARRY, 80, "approach")
186
+
187
+ elif phase == "approach":
188
+ self.height = .74
189
+ self.speed = .18
190
+ self.tx, self.ty, self.tyaw = 1.55, .74, -1.57
191
+ self._goto("pitch_place", 250)
192
+
193
+ elif phase == "pitch_place":
194
+ self.tx = float(self.low[21])
195
+ self.ty = float(self.low[22])
196
+ self.speed = .12
197
+ self.height = (.74, .80, .86)[min(self.placed, 2)]
198
+ self.pitch = 0.
199
+ if self.k + 1 >= 40:
200
+ self._go("release")
201
+
202
+ elif phase == "release":
203
+ self.grip_l = 0.
204
+ self.pitch = 0.
205
+ if self.k + 1 >= 60:
206
+ self.placed += 1
207
+ self._go("unpitch")
208
+
209
+ elif phase == "unpitch":
210
+ self.grip_l = 0.
211
+ self.pitch = 0.
212
+ self.height = (.74, .80, .86)[min(self.placed - 1, 2)]
213
+ if self.k + 1 >= 60:
214
+ self._go("finish" if self.placed >= 3 else "retreat")
215
+
216
+ elif phase == "retreat":
217
+ self.grip_l = 0.
218
+ self.height = .74
219
+ self.pitch = 0.
220
+ self.speed = .30
221
+ self.ty = 1.0
222
+ self._goto("return_turn", 180)
223
+
224
+ elif phase == "return_turn":
225
+ self.speed = .30
226
+ self.tx, self.ty, self.tyaw = 1.55, 1.0, 0.
227
+ self._goto("return_x", 180)
228
+
229
+ elif phase == "return_x":
230
+ self.speed = .40
231
+ self.tx, self.ty, self.tyaw = .05, 1.0, 0.
232
+ self._goto("return_y", 300)
233
+
234
+ elif phase == "return_y":
235
+ self.speed = .40
236
+ self.tx, self.ty, self.tyaw = .05, 0., 0.
237
+ self._goto("home", 220)
238
+
239
+ elif phase == "finish":
240
+ self.grip_l = 0.
241
+ self.height = .86
242
+ self.pitch = 0.
243
+ self.speed = .12
244
+
245
+ action = self._base_action()
246
+ self.k += 1
247
+ return action
policies/stack_blocks/opus-s1/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ seed,success,length,termination,fell
2
+ 620000,1,5784,success,0
3
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4
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62
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63
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68
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69
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70
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71
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72
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73
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75
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76
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77
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78
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79
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80
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81
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+ 620097,1,5791,success,0
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+ 620098,1,5612,success,0
101
+ 620099,1,5809,success,0
policies/stack_blocks/opus-s1/g1kin.py ADDED
@@ -0,0 +1,97 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Approximate G1 arm kinematics (pelvis frame), from the published G1 model."""
2
+ import numpy as np
3
+
4
+
5
+ def rx(a):
6
+ c, s = np.cos(a), np.sin(a)
7
+ return np.array([[1, 0, 0], [0, c, -s], [0, s, c]])
8
+
9
+
10
+ def ry(a):
11
+ c, s = np.cos(a), np.sin(a)
12
+ return np.array([[c, 0, s], [0, 1, 0], [-s, 0, c]])
13
+
14
+
15
+ def rz(a):
16
+ c, s = np.cos(a), np.sin(a)
17
+ return np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]])
18
+
19
+
20
+ def quat(w, x, y, z):
21
+ q = np.array([w, x, y, z]) / np.linalg.norm([w, x, y, z])
22
+ w, x, y, z = q
23
+ return np.array([
24
+ [1 - 2 * (y * y + z * z), 2 * (x * y - z * w), 2 * (x * z + y * w)],
25
+ [2 * (x * y + z * w), 1 - 2 * (x * x + z * z), 2 * (y * z - x * w)],
26
+ [2 * (x * z - y * w), 2 * (y * z + x * w), 1 - 2 * (x * x + y * y)],
27
+ ])
28
+
29
+
30
+ HAND_OFF = np.array([0.0415 + 0.10, 0.0, 0.0]) # grasp point beyond wrist yaw link (to calibrate)
31
+
32
+
33
+ def torso_frame(waist):
34
+ """Torso pose in pelvis frame from waist (yaw, roll, pitch)."""
35
+ R = rz(waist[0])
36
+ p = np.zeros(3)
37
+ p = p + R @ np.array([-0.0039635, 0, 0.035])
38
+ R = R @ rx(waist[1])
39
+ p = p + R @ np.array([0, 0, 0.019])
40
+ R = R @ ry(waist[2])
41
+ return p, R
42
+
43
+
44
+ def arm_chain(q, side, waist=(0, 0, 0), hand_off=None):
45
+ """Return list of (p, R) frames: torso, shoulder pitch..wrist yaw, hand point."""
46
+ sgn = 1.0 if side == 'left' else -1.0
47
+ p, R = torso_frame(waist)
48
+ frames = []
49
+ def step(off, Rfix, axis, ang):
50
+ nonlocal p, R
51
+ p = p + R @ np.asarray(off)
52
+ R = R @ Rfix
53
+ R = R @ {'x': rx, 'y': ry, 'z': rz}[axis](ang)
54
+ frames.append((p.copy(), R.copy()))
55
+ I = np.eye(3)
56
+ step([0.0039563, sgn * 0.10022, 0.24778], quat(0.990264, sgn * 0.139201, 0, 0), 'y', q[0])
57
+ step([0, sgn * 0.038, -0.013831], quat(0.990268, -sgn * 0.139172, 0, 0), 'x', q[1])
58
+ step([0, sgn * 0.00624, -0.1032], I, 'z', q[2])
59
+ step([0.015783, 0, -0.080518], I, 'y', q[3])
60
+ step([0.1, sgn * 0.00188791, -0.01], I, 'x', q[4])
61
+ step([0.038, 0, 0], I, 'y', q[5])
62
+ step([0.046, 0, 0], I, 'z', q[6])
63
+ ho = HAND_OFF if hand_off is None else hand_off
64
+ frames.append((p + R @ ho, R.copy()))
65
+ return frames
66
+
67
+
68
+ def hand_pose(q, side, waist=(0, 0, 0), hand_off=None):
69
+ return arm_chain(q, side, waist, hand_off)[-1]
70
+
71
+
72
+ # shoulder roll is limited by the arm hitting the torso (measured: about -0.15 / +0.15)
73
+ LIM_LO = {'left': np.array([-3.0, 0.03, -2.6, -1.0, -1.9, -1.6, -1.6]),
74
+ 'right': np.array([-3.0, -2.2, -2.6, -1.0, -1.9, -1.6, -1.6])}
75
+ LIM_HI = {'left': np.array([2.6, 2.2, 2.6, 2.0, 1.9, 1.6, 1.6]),
76
+ 'right': np.array([2.6, -0.03, 2.6, 2.0, 1.9, 1.6, 1.6])}
77
+
78
+
79
+ def ik(p_target, side, q0, waist=(0, 0, 0), xdir=None, ydir=None, wdir=0.3,
80
+ reg=0.01, qref=None, hand_off=None):
81
+ """Numeric IK for the hand point; xdir: desired gripper axis, ydir: desired jaw axis."""
82
+ from scipy.optimize import least_squares
83
+ qref = q0 if qref is None else qref
84
+ def res(q):
85
+ p, R = hand_pose(q, side, waist, hand_off)
86
+ r = [p - p_target]
87
+ if xdir is not None:
88
+ r.append(wdir * (R[:, 0] - xdir))
89
+ if ydir is not None:
90
+ r.append(wdir * (R[:, 1] - ydir))
91
+ r.append(np.asarray(reg) * (q - qref))
92
+ return np.concatenate(r)
93
+ lo, hi = LIM_LO[side], LIM_HI[side]
94
+ x0 = np.clip(q0, lo + 1e-3, hi - 1e-3)
95
+ sol = least_squares(res, x0, bounds=(lo, hi), xtol=1e-8, ftol=1e-10)
96
+ p, R = hand_pose(sol.x, side, waist, hand_off)
97
+ return sol.x, np.linalg.norm(p - p_target)
policies/stack_blocks/opus-s1/headcam.py ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Head camera model (fitted constants) and helpers."""
2
+ import numpy as np
3
+ from g1kin import rx, ry, rz, torso_frame
4
+
5
+ W = H = 84
6
+ R0 = np.column_stack([[0, -1, 0], [0, 0, 1], [-1, 0, 0]])
7
+ HEAD = np.array([0.1131, 0.0043, 0.3301, 0.9779, -0.013, -0.0035, 1.0932, -0.0263])
8
+
9
+
10
+ def pelvis_world(o, kp=HEAD[7]):
11
+ """Pelvis rotation and position in world from low_dim_obs."""
12
+ Rp = rz(o[24]) @ ry(kp * o[2])
13
+ return np.array([o[21], o[22], o[23]]), Rp
14
+
15
+
16
+ def cam_world(o, p=HEAD):
17
+ cx, cy, cz, th, ps, ph, fov, kp = p[:8]
18
+ tp, tR = torso_frame(o[0:3])
19
+ pw, Rp = pelvis_world(o, kp)
20
+ Rc_t = ry(th) @ rz(ps) @ R0 @ rz(ph)
21
+ Rw = Rp @ tR @ Rc_t
22
+ cw = pw + Rp @ (tp + tR @ np.array([cx, cy, cz]))
23
+ return cw, Rw, fov
24
+
25
+
26
+ def project(o, X, p=HEAD):
27
+ cw, Rw, fov = cam_world(o, p)
28
+ xc = Rw.T @ (np.asarray(X) - cw)
29
+ f = (H / 2) / np.tan(fov / 2)
30
+ return np.array([W / 2 + f * xc[0] / -xc[2], H / 2 - f * xc[1] / -xc[2]]), -xc[2]
31
+
32
+
33
+ def ray(o, uv, p=HEAD):
34
+ """World ray (origin, unit dir) through pixel uv."""
35
+ cw, Rw, fov = cam_world(o, p)
36
+ f = (H / 2) / np.tan(fov / 2)
37
+ d = np.array([(uv[0] - W / 2) / f, -(uv[1] - H / 2) / f, -1.0])
38
+ d = Rw @ d
39
+ return cw, d / np.linalg.norm(d)
40
+
41
+
42
+ def pixel_to_plane(o, uv, z, p=HEAD):
43
+ c, d = ray(o, uv, p)
44
+ t = (z - c[2]) / d[2]
45
+ return c + t * d
46
+
47
+
48
+ def to_world(o, p_pelvis):
49
+ pw, Rp = pelvis_world(o)
50
+ return pw + Rp @ np.asarray(p_pelvis)
51
+
52
+
53
+ def to_pelvis(o, X):
54
+ pw, Rp = pelvis_world(o)
55
+ return Rp.T @ (np.asarray(X) - pw)
policies/stack_blocks/opus-s1/percept.py ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Image processing helpers."""
2
+ import cv2
3
+ import numpy as np
4
+
5
+ W = H = 84
6
+
7
+
8
+ def red_mask(im):
9
+ r, g, b = [im[..., i].astype(int) for i in range(3)]
10
+ return ((r > 110) & (g < 80) & (b < 80) & (r > 2 * g)).astype(np.uint8)
11
+
12
+
13
+ def top_mask(im):
14
+ """The brightly lit top face of a red block."""
15
+ r, g, b = [im[..., i].astype(int) for i in range(3)]
16
+ return ((r > 225) & (g < 110) & (b < 110)).astype(np.uint8)
17
+
18
+
19
+ def green_mask(im):
20
+ r, g, b = [im[..., i].astype(int) for i in range(3)]
21
+ return ((g > 120) & (g > r + 40) & (g > b + 40)).astype(np.uint8)
22
+
23
+
24
+ def blobs(mask, min_area=4, border_ok=False):
25
+ n, lab, st, cen = cv2.connectedComponentsWithStats(mask)
26
+ out = []
27
+ for i in range(1, n):
28
+ x, y, w, h, area = st[i]
29
+ touches = x == 0 or y == 0 or x + w >= mask.shape[1] or y + h >= mask.shape[0]
30
+ if area < min_area or (touches and not border_ok):
31
+ continue
32
+ out.append({'c': cen[i], 'area': int(area), 'bbox': (x, y, w, h), 'touch': bool(touches), 'mask': lab == i})
33
+ return out
policies/stack_blocks/opus-s1/policy.py ADDED
@@ -0,0 +1,503 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Scripted policy for the G1 stack-blocks task.
2
+
3
+ Pipeline: detect blocks with the head camera, grasp one block per hand with a
4
+ tilted top-down approach, walk around the tables to the green pad, place one
5
+ block on the pad and stack the second on it, then fetch the third block.
6
+ """
7
+
8
+ import numpy as np
9
+
10
+ import g1kin
11
+ import headcam
12
+ import percept
13
+
14
+ TABLE_Z = 0.707 # table top (world z)
15
+ BLOCK = 0.05 # block edge
16
+ BLOCK_Z = TABLE_Z + BLOCK / 2
17
+ HO_G = np.array([0.195, 0.0, 0.0]) # jaw centre beyond the wrist yaw link
18
+ TILT = 0.9 # gripper tilt from vertical during grasp/place
19
+ REACH = np.array([0.36, 0.20]) # target position in pelvis frame for grasping (y sign per side)
20
+ JAW_WIN = np.array([-47.0, 47.0]) # allowed jaw rotation (deg) for the right hand; mirrored for left
21
+ REG = np.array([0.006, 0.006, 0.015, 0.006, 0.015, 0.006, 0.015])
22
+ QN_R = np.array([-0.5, -0.1, 0, 0.3, 0, 0.8, 0])
23
+ TUCK_R = np.array([-0.2, -0.2, 0, -0.9, 0, 0, 0])
24
+ N_BLOCKS = 3
25
+ GRASP_DEPTH = 0.012 # jaw centre beyond block centre along the approach axis
26
+ GRIP_OK = (0.12, 0.6) # gripper state range of a square grasp on a block
27
+ LOOK = np.array([0.03, 0.14]) # wrist-camera look pose: back along heading, up
28
+ CARRY = np.array([0.33, 0.22, 0.24]) # held block position in pelvis frame while carrying
29
+ MIRROR = np.array([1, -1, -1, 1, -1, 1, -1])
30
+ SL = {'left': slice(5, 12), 'right': slice(12, 19)}
31
+ OBS_SL = {'left': slice(3, 10), 'right': slice(12, 19)}
32
+ GRIP = {'left': 19, 'right': 20}
33
+ GRIP_OBS = {'left': 50, 'right': 51}
34
+
35
+
36
+ def wrap(x):
37
+ return (x + np.pi) % (2 * np.pi) - np.pi
38
+
39
+
40
+ def side_vec(side, v):
41
+ v = np.array(v, dtype=float)
42
+ return v if side == 'right' else v * MIRROR
43
+
44
+
45
+ class Policy:
46
+ def reset(self, obs, tools):
47
+ self.tools = tools
48
+ self.obs = obs
49
+ self.a = np.asarray(tools.hold_action(), dtype=np.float64)
50
+ self.a[0] = self.a[1] = self.a[3] = 0.0
51
+ self.a[4] = 0.0
52
+ self.gen = self.script()
53
+ self.log = []
54
+ self.bias = {'left': np.zeros(7), 'right': np.zeros(7)}
55
+ self.ik_err = 0.0
56
+
57
+ def act(self, obs, tools):
58
+ self.obs = obs
59
+ try:
60
+ a = next(self.gen)
61
+ except StopIteration:
62
+ self.finished = True
63
+ self.gen = self.idle()
64
+ a = next(self.gen)
65
+ except Exception as exc: # never crash the episode: hold still instead
66
+ self.note('error', repr(exc))
67
+ self.finished = True
68
+ self.gen = self.idle()
69
+ a = next(self.gen)
70
+ return np.asarray(a, dtype=np.float32)
71
+
72
+ # ------------------------------------------------------------------ basics
73
+ def lo(self):
74
+ return np.asarray(self.obs['low_dim_obs'], dtype=np.float64)
75
+
76
+ def idle(self):
77
+ while True:
78
+ self.a[0] = self.a[1] = self.a[3] = 0.0
79
+ yield self.a.copy()
80
+
81
+ def wait(self, n):
82
+ for _ in range(n):
83
+ yield self.a.copy()
84
+
85
+ def note(self, *args):
86
+ self.log.append((int(self.obs['t']),) + args)
87
+
88
+ def goto(self, x, y, yaw, tol=0.03, ytol=0.05, maxn=1500, vmax=0.5, settle=20):
89
+ a = self.a
90
+ for i in range(maxn):
91
+ o = self.lo()
92
+ px, py, pyaw = o[21], o[22], o[24]
93
+ dx, dy = x - px, y - py
94
+ c, s = np.cos(pyaw), np.sin(pyaw)
95
+ bx, by = c * dx + s * dy, -s * dx + c * dy
96
+ dyaw = wrap(yaw - pyaw)
97
+ d = np.hypot(bx, by)
98
+ if d < tol and abs(dyaw) < ytol:
99
+ break
100
+ v = np.array([bx, by]) * 1.5
101
+ n = np.linalg.norm(v)
102
+ if n > vmax:
103
+ v *= vmax / n
104
+ if d >= tol and n < 0.07:
105
+ v *= 0.07 / max(n, 1e-6)
106
+ if d < tol:
107
+ v[:] = 0
108
+ a[0], a[1] = v
109
+ a[3] = np.clip(2.0 * dyaw, -0.8, 0.8)
110
+ yield a.copy()
111
+ a[0] = a[1] = a[3] = 0.0
112
+ yield from self.wait(settle)
113
+
114
+ def walk_path(self, pts, vmax=0.5):
115
+ """Walk through waypoints facing the direction of travel, then final yaw."""
116
+ a = self.a
117
+ for i, (x, y, yaw) in enumerate(pts):
118
+ last = i == len(pts) - 1
119
+ for _ in range(1500):
120
+ o = self.lo()
121
+ dx, dy = x - o[21], y - o[22]
122
+ d = np.hypot(dx, dy)
123
+ if d < (0.25 if last else 0.15):
124
+ break
125
+ err = wrap(np.arctan2(dy, dx) - o[24])
126
+ a[3] = np.clip(2.0 * err, -0.8, 0.8)
127
+ a[0] = vmax * max(0.0, np.cos(err)) ** 4 if abs(err) < 0.6 else 0.0
128
+ if 0 < a[0] < 0.07:
129
+ a[0] = 0.0
130
+ a[0] = min(a[0], 0.8 * d + 0.1)
131
+ a[1] = 0.0
132
+ yield a.copy()
133
+ if last:
134
+ yield from self.goto(x, y, yaw, tol=0.04, ytol=0.05, vmax=0.25, settle=10)
135
+
136
+ def turn_to(self, yaw):
137
+ a = self.a
138
+ for i in range(400):
139
+ dyaw = wrap(yaw - self.lo()[24])
140
+ if abs(dyaw) < 0.1:
141
+ break
142
+ a[0] = a[1] = 0.0
143
+ a[3] = np.clip(2.0 * dyaw, -0.8, 0.8)
144
+ yield a.copy()
145
+ a[3] = 0.0
146
+
147
+ # ------------------------------------------------------------------ arms
148
+ def fk_world(self, side, ho=HO_G):
149
+ o = self.lo()
150
+ fr = g1kin.arm_chain(o[OBS_SL[side]], side, o[0:3], ho)
151
+ return headcam.to_world(o, fr[-1][0])
152
+
153
+ def axis_world(self, jaw):
154
+ h = np.array([np.sin(jaw), -np.cos(jaw), 0.0])
155
+ xd = np.sin(TILT) * h + np.array([0, 0, -np.cos(TILT)])
156
+ yd = np.array([np.cos(jaw), np.sin(jaw), 0.0])
157
+ return xd, yd
158
+
159
+ def ik_world(self, Xw, side, jaw):
160
+ o = self.lo()
161
+ Xp = headcam.to_pelvis(o, Xw)
162
+ _, Rp = headcam.pelvis_world(o)
163
+ xd, yd = self.axis_world(jaw)
164
+ qn = side_vec(side, QN_R)
165
+ best = None
166
+ seeds = (self.a[SL[side]] - self.bias[side], qn,
167
+ side_vec(side, [-0.8, -0.3, 0.5, 0.8, 0, 0.8, 0.5]),
168
+ side_vec(side, [-0.8, -0.3, -0.5, 0.8, 0, 0.8, -0.5]))
169
+ for q0 in seeds:
170
+ q, e = g1kin.ik(Xp, side, q0, o[0:3], xdir=Rp.T @ xd, ydir=Rp.T @ yd,
171
+ reg=REG, qref=qn, hand_off=HO_G)
172
+ if best is None or e < best[1] - 3e-3:
173
+ best = (q, e)
174
+ return best
175
+
176
+ def move_arm(self, side, q, n=40, steps=5):
177
+ sl = SL[side]
178
+ q0 = self.a[sl].copy()
179
+ k = max(1, n // steps)
180
+ for s in np.linspace(0, 1, steps + 1)[1:]:
181
+ self.a[sl] = (1 - s) * q0 + s * np.asarray(q)
182
+ yield from self.wait(k)
183
+
184
+ def reach(self, side, X, jaw, n=40, iters=2, steps=5, maxcorr=0.12):
185
+ """Move the jaw centre to X; joint-space integral term removes gravity sag."""
186
+ X = np.asarray(X, dtype=float)
187
+ q, e = self.ik_world(X, side, jaw)
188
+ bias = self.bias[side]
189
+ yield from self.move_arm(side, q + bias, n, steps)
190
+ for _ in range(iters):
191
+ yield from self.wait(8)
192
+ qm = self.lo()[OBS_SL[side]]
193
+ bias = np.clip(bias + (q - qm), -maxcorr, maxcorr)
194
+ yield from self.move_arm(side, q + bias, 16, 2)
195
+ self.bias[side] = bias
196
+ self.ik_err = e
197
+
198
+ def tuck(self, side, n=40):
199
+ yield from self.move_arm(side, side_vec(side, TUCK_R), n)
200
+
201
+ # ------------------------------------------------------------------ perception
202
+ def head_blobs_border(self, mask_fn, z):
203
+ return self.head_blobs(mask_fn, z, border_ok=True)
204
+
205
+ def head_blobs(self, mask_fn, z, border_ok=False):
206
+ import cv2
207
+ o = self.lo()
208
+ im = self.tools.image('head')
209
+ mask = mask_fn(im)
210
+ for side in ('left', 'right'):
211
+ gs = o[GRIP_OBS[side]]
212
+ if 0.05 < gs < 0.9: # holding a block: ignore it
213
+ uv, d = headcam.project(o, self.fk_world(side))
214
+ if d > 0:
215
+ cv2.circle(mask, (int(round(uv[0])), int(round(uv[1]))), 11, 0, -1)
216
+ out = []
217
+ for b in percept.blobs(mask, border_ok=border_ok):
218
+ X = headcam.pixel_to_plane(o, b['c'], z)
219
+ out.append({'X': X, 'uv': b['c'], 'area': b['area'], 'mask': b['mask'], 'im': im,
220
+ 'touch': b['touch']})
221
+ return out
222
+
223
+ def block_top(self, blob, ztop):
224
+ """Top-face centre (world) and yaw (mod pi/2) of a block blob."""
225
+ import cv2
226
+ o = self.lo()
227
+ top = percept.top_mask(blob['im']) & cv2.dilate(blob['mask'].astype(np.uint8), np.ones((3, 3), np.uint8))
228
+ ys, xs = np.nonzero(top)
229
+ if len(xs) < 5:
230
+ return None, None
231
+ pts = np.array([headcam.pixel_to_plane(o, (u, v), ztop)[:2] for u, v in zip(xs, ys)])
232
+ c = pts.mean(axis=0)
233
+ (cx, cy), (w, h), ang = cv2.minAreaRect((pts * 1000).astype(np.float32))
234
+ return np.array([c[0], c[1], ztop - BLOCK / 2]), wrap(4 * np.deg2rad(ang)) / 4
235
+
236
+ # ------------------------------------------------------------------ skills
237
+ def wrist_top(self, side):
238
+ """Top face of the most prominent block in the wrist camera: (centre, angle mod 90 rad)."""
239
+ import cv2
240
+ im = self.tools.image(side + '_wrist')
241
+ red = percept.red_mask(im)
242
+ top = percept.top_mask(im)
243
+ bl = percept.blobs(red, min_area=20, border_ok=True)
244
+ if not bl:
245
+ return None, None
246
+ b = max(bl, key=lambda b: b['area'])
247
+ m = (top & b['mask']).astype(np.uint8)
248
+ if m.sum() < 20:
249
+ return None, None
250
+ cnts, _ = cv2.findContours(m, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
251
+ cnt = max(cnts, key=cv2.contourArea)
252
+ (cx, cy), _, ang = cv2.minAreaRect(cnt)
253
+ return np.array([cx, cy]), wrap(4 * np.deg2rad(ang)) / 4
254
+
255
+ def grasp(self, side, X, jaw=np.pi / 2):
256
+ """Tilted grasp of the block whose centre is X (world)."""
257
+ g = GRIP[side]
258
+ self.a[g] = 0.0
259
+ jaw0 = jaw
260
+ for k in range(2):
261
+ h = np.array([np.sin(jaw), -np.cos(jaw), 0.0])
262
+ look = X - LOOK[0] * h + np.array([0, 0, LOOK[1]])
263
+ yield from self.reach(side, look, jaw, n=45 if k == 0 else 30)
264
+ yield from self.wait(8)
265
+ c, ang = self.wrist_top(side)
266
+ self.note('wrist', side, k, None if c is None else c.round(1).tolist(),
267
+ None if ang is None else round(float(np.rad2deg(ang)), 1))
268
+ if ang is None or abs(ang) < np.deg2rad(6):
269
+ break
270
+ jaw = jaw - ang
271
+ lo, hi = np.deg2rad(JAW_WIN) if side == 'right' else -np.deg2rad(JAW_WIN[::-1])
272
+ if jaw - jaw0 > hi:
273
+ jaw -= np.pi / 2
274
+ elif jaw - jaw0 < lo:
275
+ jaw += np.pi / 2
276
+ xd, _ = self.axis_world(jaw)
277
+ yield from self.reach(side, X - 0.08 * xd, jaw, n=30, iters=3)
278
+ yield from self.reach(side, X + GRASP_DEPTH * xd, jaw, n=30, iters=1)
279
+ at = self.fk_world(side)
280
+ self.note('at', side, at.round(3).tolist(), (at - X).round(3).tolist(), round(float(self.ik_err), 3))
281
+ self.a[g] = 1.0
282
+ yield from self.wait(30)
283
+ up = self.fk_world(side) + np.array([0, 0, 0.10])
284
+ yield from self.reach(side, up, jaw, n=30, iters=0)
285
+ yield from self.wait(10)
286
+ gs = self.lo()[GRIP_OBS[side]]
287
+ held = GRIP_OK[0] <= gs <= GRIP_OK[1]
288
+ self.note('grasp', side, held, round(float(gs), 2))
289
+ if not held:
290
+ # bad or empty grasp: put the block back down gently and back off
291
+ if gs < 0.95:
292
+ yield from self.reach(side, self.fk_world(side) - np.array([0, 0, 0.09]), jaw, n=30, iters=0)
293
+ self.a[g] = 0.0
294
+ yield from self.wait(20)
295
+ yield from self.reach(side, self.fk_world(side) - 0.06 * xd + np.array([0, 0, 0.08]), jaw, n=30, iters=0)
296
+ return held
297
+
298
+ def place(self, side, X, jaw=np.pi / 2):
299
+ """Lower the held block so its centre reaches X, release, retract."""
300
+ g = GRIP[side]
301
+ xd, _ = self.axis_world(jaw)
302
+ X = X + GRASP_DEPTH * xd # the jaw centre sits this far beyond the block centre
303
+ above = X + np.array([0, 0, 0.05])
304
+ yield from self.reach(side, above, jaw, n=40, iters=3)
305
+ self.note('above', side, self.fk_world(side).round(3).tolist(), (self.fk_world(side) - above).round(3).tolist())
306
+ yield from self.reach(side, X + np.array([0, 0, 0.008]), jaw, n=40, iters=2, steps=8)
307
+ yield from self.wait(10)
308
+ self.note('release', side, self.fk_world(side).round(3).tolist())
309
+ self.a[g] = 0.0
310
+ yield from self.wait(25)
311
+ self.note('released', side)
312
+ back = self.fk_world(side) - 0.08 * xd + np.array([0, 0, 0.03])
313
+ yield from self.reach(side, back, jaw, n=30, iters=0)
314
+ yield from self.reach(side, back + np.array([0, 0, 0.08]), jaw, n=25, iters=0)
315
+
316
+ def stand_for(self, X, side, facing):
317
+ """Pelvis pose that puts world point X at the standard reach offset."""
318
+ c, s = np.cos(facing), np.sin(facing)
319
+ ry = REACH[1] if side == 'left' else -REACH[1]
320
+ bx, by = REACH[0], ry
321
+ px = X[0] - (c * bx - s * by)
322
+ py = X[1] - (s * bx + c * by)
323
+ # do not walk into the tables
324
+ if abs(wrap(facing)) < 0.5:
325
+ px = min(px, 0.24)
326
+ else:
327
+ px = max(px, 1.92)
328
+ return px, py
329
+
330
+ # ------------------------------------------------------------------ script
331
+ def script(self):
332
+ a = self.a
333
+ a[GRIP['left']] = a[GRIP['right']] = 1.0
334
+ a[SL['left']] = side_vec('left', TUCK_R)
335
+ a[SL['right']] = side_vec('right', TUCK_R)
336
+ yield from self.wait(40)
337
+ self.level = 0
338
+ yield from self.rescan([], expected=N_BLOCKS)
339
+ blocks = self.scan
340
+ while blocks:
341
+ if len(blocks) >= 2:
342
+ plan = [('right', blocks[0]), ('left', blocks[-1])]
343
+ else:
344
+ plan = [('right', blocks[0])]
345
+ held = []
346
+ for side, X in plan:
347
+ ok = yield from self.fetch(side, X, 0.0)
348
+ if ok:
349
+ held.append(side)
350
+ blocks = [b for b in blocks if b is not X]
351
+ else:
352
+ yield from self.tuck(side)
353
+ if not held:
354
+ yield from self.rescan(blocks, expected=N_BLOCKS - self.level)
355
+ blocks = self.scan
356
+ continue
357
+ yield from self.to_table2()
358
+ for side in ['left', 'right']:
359
+ gs = self.lo()[GRIP_OBS[side]]
360
+ if side in held and GRIP_OK[0] - 0.05 <= gs <= GRIP_OK[1] + 0.2:
361
+ yield from self.put_on_stack(side, self.level)
362
+ self.level += 1
363
+ yield from self.tuck(side)
364
+ if self.level < N_BLOCKS:
365
+ yield from self.to_table1()
366
+ yield from self.rescan(blocks, expected=N_BLOCKS - self.level)
367
+ blocks = self.scan
368
+ yield from self.wait(100)
369
+
370
+ def rescan(self, guesses, expected):
371
+ """Look at table 1 (shifting sideways if needed) and list the blocks on it."""
372
+ y0 = float(np.mean([g[1] for g in guesses])) if guesses else 0.0
373
+ found = []
374
+ for dy in (0.0, -0.25, 0.25):
375
+ yield from self.goto(0.1, y0 + dy, 0.0, tol=0.05, vmax=0.3)
376
+ for b in self.head_blobs_border(percept.red_mask, BLOCK_Z):
377
+ X = b['X']
378
+ if X[0] < 0.45 or X[0] > 0.85 or b['area'] < 12:
379
+ continue
380
+ pri = 0 if not b['touch'] else 1
381
+ for k, (Xf, pf) in enumerate(found):
382
+ if np.linalg.norm(Xf[:2] - X[:2]) < 0.06:
383
+ if pri < pf:
384
+ found[k] = (X, pri)
385
+ break
386
+ else:
387
+ found.append((X, pri))
388
+ if len(found) >= expected:
389
+ break
390
+ found = [f[0] for f in found]
391
+ self.note('rescan', [f.round(3).tolist() for f in found])
392
+ self.scan = sorted(found, key=lambda X: X[1])
393
+
394
+ def to_table1(self):
395
+ o = self.lo()
396
+ yield from self.goto(o[21] + 0.15, o[22], o[24], tol=0.08, ytol=0.2, vmax=0.3, settle=0)
397
+ pts = [(2.05, 1.2, np.pi / 2), (-0.1, 1.2, np.pi), (0.1, 0.0, 0.0)]
398
+ yield from self.walk_path(pts)
399
+
400
+ def fetch(self, side, Xguess, facing):
401
+ px, py = self.stand_for(Xguess, side, facing)
402
+ yield from self.goto(px, py, facing, tol=0.03, vmax=0.25, maxn=500)
403
+ # re-detect, pick nearest blob to the guess
404
+ bl = self.head_blobs(percept.red_mask, BLOCK_Z)
405
+ yaw = 0.0
406
+ if bl:
407
+ b = min(bl, key=lambda b: np.linalg.norm(b['X'][:2] - Xguess[:2]))
408
+ if np.linalg.norm(b['X'][:2] - Xguess[:2]) < 0.1:
409
+ Xguess = b['X']
410
+ Xt, yt = self.block_top(b, TABLE_Z + BLOCK)
411
+ if Xt is not None:
412
+ Xguess, yaw = Xt, yt
413
+ jaw = facing + np.pi / 2
414
+ self.note('fetch', side, Xguess.round(3).tolist(), round(float(yaw), 3), self.lo()[21:25].round(3).tolist())
415
+ ok = yield from self.grasp(side, Xguess, jaw)
416
+ if not ok:
417
+ # second attempt with a fresh detection
418
+ yield from self.tuck(side, 30)
419
+ bl = self.head_blobs(percept.red_mask, BLOCK_Z)
420
+ if bl:
421
+ b = min(bl, key=lambda b: np.linalg.norm(b['X'][:2] - Xguess[:2]))
422
+ if np.linalg.norm(b['X'][:2] - Xguess[:2]) < 0.1:
423
+ Xt, _ = self.block_top(b, TABLE_Z + BLOCK)
424
+ Xguess = Xt if Xt is not None else b['X']
425
+ ok = yield from self.grasp(side, Xguess, jaw)
426
+ if ok:
427
+ yield from self.carry(side)
428
+ return ok
429
+
430
+ def carry(self, side):
431
+ """Lift the held block high, out of the head camera's view."""
432
+ o = self.lo()
433
+ sy = 1.0 if side == 'left' else -1.0
434
+ Xw = headcam.to_world(o, np.array([CARRY[0], sy * CARRY[1], CARRY[2]]))
435
+ jaw = o[24] + np.pi / 2
436
+ yield from self.reach(side, Xw, jaw, n=40, iters=0)
437
+
438
+ def to_table2(self):
439
+ o = self.lo()
440
+ yield from self.goto(o[21] - 0.25, o[22], o[24], tol=0.08, ytol=0.2, vmax=0.3, settle=0)
441
+ pts = [(o[21] - 0.25, 1.2, np.pi / 2), (2.05, 1.2, 0.0), (2.05, 0.0, np.pi)]
442
+ yield from self.walk_path(pts)
443
+ if not hasattr(self, 'pad'):
444
+ yield from self.find_pad()
445
+
446
+ def find_pad(self):
447
+ """Locate the green pad from the arrival pose (fully in view)."""
448
+ self.pad = np.array([1.55, 0.0, TABLE_Z])
449
+ for k in range(3):
450
+ g = [b for b in self.head_blobs_border(percept.green_mask, TABLE_Z) if b['area'] >= 15]
451
+ if not g:
452
+ break
453
+ b = max(g, key=lambda b: b['area'])
454
+ self.pad = b['X']
455
+ if not b['touch']:
456
+ break
457
+ o = self.lo()
458
+ yield from self.goto(o[21], 0.5 * (o[22] + b['X'][1]), np.pi, tol=0.03, vmax=0.25)
459
+ self.note('pad', self.pad.round(3).tolist())
460
+
461
+ def stack_top(self, level):
462
+ """Top-face centre of the stack that currently has `level` blocks."""
463
+ ztop = TABLE_Z + BLOCK * level
464
+ T = getattr(self, 'top', self.pad)
465
+ bl = self.head_blobs(percept.red_mask, ztop - BLOCK / 2)
466
+ best = None
467
+ for b in bl:
468
+ Xt, _ = self.block_top(b, ztop)
469
+ if Xt is None:
470
+ continue
471
+ d = np.linalg.norm(Xt[:2] - self.pad[:2])
472
+ if d < 0.1 and (best is None or d < best[0]):
473
+ best = (d, Xt)
474
+ if best is not None:
475
+ T = best[1]
476
+ self.top = T
477
+ return T
478
+
479
+ def put_on_stack(self, side, level):
480
+ facing = np.pi
481
+ if level == 0:
482
+ T = self.pad
483
+ else:
484
+ T = self.stack_top(level)
485
+ px, py = self.stand_for(T, side, facing)
486
+ yield from self.goto(px, py, facing, tol=0.03, vmax=0.25, maxn=500)
487
+ # refine target after moving
488
+ if level == 0:
489
+ g = self.head_blobs(percept.green_mask, TABLE_Z)
490
+ g = [b for b in g if b['area'] >= 30 and np.linalg.norm(b['X'][:2] - self.pad[:2]) < 0.05]
491
+ if g:
492
+ T = max(g, key=lambda b: b['area'])['X']
493
+ else:
494
+ T = self.stack_top(level)
495
+ last = getattr(self, 'placed', None)
496
+ if last is not None and np.linalg.norm(T[:2] - last[:2]) < 0.04:
497
+ # the previous block sits where we put it; the head camera is less
498
+ # precise than the arm, so lean on the commanded position
499
+ T = 0.7 * last + 0.3 * T
500
+ X = np.array([T[0], T[1], BLOCK_Z + BLOCK * level])
501
+ self.placed = X.copy()
502
+ self.note('place', side, level, X.round(3).tolist())
503
+ yield from self.place(side, X, jaw=-np.pi / 2)
policies/stack_blocks/opus-s2/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ seed,success,length,termination,fell
2
+ 620000,0,8750,timeout,0
3
+ 620001,0,8750,timeout,0
4
+ 620002,0,8750,timeout,0
5
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15
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28
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30
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31
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34
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35
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40
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44
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50
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54
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63
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65
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66
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70
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74
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75
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77
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88
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89
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90
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91
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92
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94
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95
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96
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97
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100
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101
+ 620099,0,8750,timeout,0
policies/stack_blocks/opus-s2/policy.py ADDED
@@ -0,0 +1,686 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Stack-blocks policy for the G1: scripted state machine (no learned parts).
2
+
3
+ Geometry: arm forward kinematics of the G1 (link offsets of the Unitree model),
4
+ a pinhole model of the head camera (hand-tuned constants), damped least-squares
5
+ IK, colour segmentation of the red blocks and the green pad, closed-loop base
6
+ navigation on the pelvis pose and wrist-camera centring before each grasp.
7
+ """
8
+
9
+ import cv2
10
+ import numpy as np
11
+
12
+ # ----------------------------------------------------------------------------
13
+ # kinematics
14
+
15
+
16
+ def rx(a):
17
+ c, s = np.cos(a), np.sin(a)
18
+ return np.array([[1, 0, 0], [0, c, -s], [0, s, c]])
19
+
20
+
21
+ def ry(a):
22
+ c, s = np.cos(a), np.sin(a)
23
+ return np.array([[c, 0, s], [0, 1, 0], [-s, 0, c]])
24
+
25
+
26
+ def rz(a):
27
+ c, s = np.cos(a), np.sin(a)
28
+ return np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]])
29
+
30
+
31
+ def quat(w, x, y, z):
32
+ q = np.array([w, x, y, z]) / np.linalg.norm([w, x, y, z])
33
+ w, x, y, z = q
34
+ return np.array([[1 - 2 * (y * y + z * z), 2 * (x * y - z * w), 2 * (x * z + y * w)],
35
+ [2 * (x * y + z * w), 1 - 2 * (x * x + z * z), 2 * (y * z - x * w)],
36
+ [2 * (x * z - y * w), 2 * (y * z + x * w), 1 - 2 * (x * x + y * y)]])
37
+
38
+
39
+ def T(R=None, p=(0, 0, 0)):
40
+ M = np.eye(4)
41
+ if R is not None:
42
+ M[:3, :3] = R
43
+ M[:3, 3] = p
44
+ return M
45
+
46
+
47
+ def torso_T(waist):
48
+ return (T(rz(waist[0])) @ T(None, (-0.0039635, 0, 0.035)) @ T(rx(waist[1]))
49
+ @ T(None, (0, 0, 0.019)) @ T(ry(waist[2])))
50
+
51
+
52
+ _SH = {s: quat(0.990264, s * 0.139201, 0, 0) for s in (1, -1)}
53
+ _SR = {s: quat(0.990268, -s * 0.139172, 0, 0) for s in (1, -1)}
54
+ HAND = 0.24 # wrist_yaw_link -> centre of a grasped block, along the fingers
55
+ HAND_Z = 0.017 # ... and along the hand z axis (measured from a grasp)
56
+
57
+
58
+ def hand_pose(q, side, waist, hand=None):
59
+ if hand is None:
60
+ hand = HAND
61
+ s = side
62
+ M = torso_T(waist)
63
+ M = M @ T(_SH[s], (0.0039563, s * 0.10022, 0.23778)) @ T(ry(q[0]))
64
+ M = M @ T(_SR[s], (0, s * 0.038, -0.013831)) @ T(rx(q[1]))
65
+ M = M @ T(None, (0, s * 0.00624, -0.1032)) @ T(rz(q[2]))
66
+ M = M @ T(None, (0.015783, 0, -0.080518)) @ T(ry(q[3]))
67
+ M = M @ T(None, (0.1, s * 0.00188791, -0.01)) @ T(rx(q[4]))
68
+ M = M @ T(None, (0.038, 0, 0)) @ T(ry(q[5]))
69
+ M = M @ T(None, (0.046, 0, 0)) @ T(rz(q[6]))
70
+ return M @ T(None, (hand, 0, HAND_Z))
71
+
72
+
73
+ QLO = np.array([-3.0892, -1.5882, -2.618, -1.0472, -1.97222, -1.61443, -1.61443])
74
+ QHI = np.array([2.6704, 2.2515, 2.618, 2.0944, 1.97222, 1.61443, 1.61443])
75
+
76
+
77
+ def rot_err(R, Rt):
78
+ E = Rt @ R.T
79
+ return 0.5 * np.array([E[2, 1] - E[1, 2], E[0, 2] - E[2, 0], E[1, 0] - E[0, 1]])
80
+
81
+
82
+ def ik(p_t, R_t, side, waist, q0, iters=12, wrot=0.3):
83
+ q = np.clip(np.array(q0, float), QLO + 0.02, QHI - 0.02)
84
+ for _ in range(iters):
85
+ M = hand_pose(q, side, waist)
86
+ e = np.r_[p_t - M[:3, 3], wrot * rot_err(M[:3, :3], R_t)]
87
+ if np.linalg.norm(e) < 1e-4:
88
+ break
89
+ J = np.zeros((6, 7))
90
+ for k in range(7):
91
+ dq = np.zeros(7)
92
+ dq[k] = 1e-4
93
+ M2 = hand_pose(q + dq, side, waist)
94
+ J[:3, k] = (M2[:3, 3] - M[:3, 3]) / 1e-4
95
+ J[3:, k] = -wrot * rot_err(M2[:3, :3], M[:3, :3]) / 1e-4
96
+ Jp = J.T @ np.linalg.inv(J @ J.T + 0.02 * np.eye(6))
97
+ dq = Jp @ e
98
+ # stay near a comfortable posture in the null space
99
+ rest = Q_REST * np.array([1, side, side, 1, side, 1, side])
100
+ dq += (np.eye(7) - Jp @ J) @ (NS_GAIN * (rest - q))
101
+ q = np.clip(q + dq, QLO + 0.02, QHI - 0.02)
102
+ return q
103
+
104
+
105
+ Q_REST = np.array([-0.5, 0.25, 0.0, 0.6, 0.0, 0.6, 0.0])
106
+ NS_GAIN = 0.0
107
+
108
+
109
+ # ----------------------------------------------------------------------------
110
+ # head camera (torso frame), constants tuned against the robot's own arm
111
+
112
+ CAMP = np.array([0.101472, 0.010612, 0.364666, 1.004299, -0.003941, 0.002519, 1.063025])
113
+ CAM_R = rz(CAMP[4]) @ ry(CAMP[3]) @ rx(CAMP[5])
114
+ CAM_F = 42.0 / np.tan(CAMP[6] / 2)
115
+
116
+
117
+ def pixel_ray(u, v, waist):
118
+ d_cam = np.array([1.0, -(u - 42) / CAM_F, -(v - 42) / CAM_F])
119
+ Tt = torso_T(waist)
120
+ R = Tt[:3, :3] @ CAM_R
121
+ o = Tt[:3, :3] @ CAMP[:3] + Tt[:3, 3]
122
+ return o, R @ d_cam
123
+
124
+
125
+ def pixel_to_plane(u, v, waist, z):
126
+ o, d = pixel_ray(u, v, waist)
127
+ return o + (z - o[2]) / d[2] * d
128
+
129
+
130
+ def project(pp, waist):
131
+ Tt = torso_T(waist)
132
+ pt = Tt[:3, :3].T @ (pp - Tt[:3, 3])
133
+ d = CAM_R.T @ (pt - CAMP[:3])
134
+ return np.array([42 + CAM_F * (-d[1]) / d[0], 42 - CAM_F * d[2] / d[0]])
135
+
136
+
137
+ # ----------------------------------------------------------------------------
138
+ # vision
139
+
140
+
141
+ def red_mask(im):
142
+ r, g, b = [im[..., i].astype(int) for i in range(3)]
143
+ return ((r > 100) & (r > 2 * g + 20) & (r > 2 * b + 20)).astype(np.uint8)
144
+
145
+
146
+ def green_mask(im):
147
+ r, g, b = [im[..., i].astype(int) for i in range(3)]
148
+ return ((g > 120) & (g > r + 40) & (g > b + 40)).astype(np.uint8)
149
+
150
+
151
+ def blobs(mask, min_area=6):
152
+ n, lab, st, cen = cv2.connectedComponentsWithStats(mask)
153
+ out = []
154
+ for i in range(1, n):
155
+ x, y, w, hh, area = st[i]
156
+ if area < min_area:
157
+ continue
158
+ edge = x == 0 or y == 0 or x + w >= mask.shape[1] or y + hh >= mask.shape[0]
159
+ out.append(dict(c=cen[i], area=int(area), bbox=(x, y, w, hh), edge=bool(edge)))
160
+ return out
161
+
162
+
163
+ def wrap(a):
164
+ return (a + np.pi) % (2 * np.pi) - np.pi
165
+
166
+
167
+ # ----------------------------------------------------------------------------
168
+ # task constants (FK-world frame)
169
+
170
+ Z_TABLE = 0.655 # table top
171
+ BLOCK = 0.05 # block edge
172
+ TILT = np.radians(45) # gripper pitch below horizontal while placing
173
+ GTILT = np.radians(45) # gripper pitch below horizontal while grasping
174
+ PITCH = 0.15 # torso pitch while manipulating
175
+ REACH = 0.42 # pelvis->target forward distance used to stand
176
+ SIDE_OFF = 0.15 # lateral offset of the hand used w.r.t. the pelvis
177
+ CAMS = {1: 'left_wrist', -1: 'right_wrist'}
178
+ DEBUG = False
179
+ PRE = 0.108 # height above the block of the wrist-camera reference view
180
+ REF_UV = (43.0, 37.3) # block centre in the wrist image, hand PRE above a good grasp
181
+ PAD_OFF = np.zeros(3) # pad centre vs. the wrist-servo estimate
182
+ SERVO_GAIN = 430.0 # wrist image px per metre of hand motion at PRE
183
+ CORR_GAIN = 0.07 # per-step integral gain of the gravity-sag correction
184
+
185
+
186
+ class Policy:
187
+ """Scripted policy: generator-based state machine."""
188
+
189
+ def reset(self, obs, tools):
190
+ self.tools = tools
191
+ self.obs = obs
192
+ self.a = np.asarray(tools.hold_action(), dtype=np.float64)
193
+ lo = obs['low_dim_obs']
194
+ self.q = {1: lo[3:10].astype(float).copy(), -1: lo[12:19].astype(float).copy()}
195
+ self.corr = {1: np.zeros(3), -1: np.zeros(3)}
196
+ self.tgt = {1: None, -1: None}
197
+ self.frame = {1: 'world', -1: 'world'}
198
+ self.R = {1: ry(TILT), -1: ry(TILT)}
199
+ self.grip = {1: 0.0, -1: 0.0}
200
+ self.base_cmd = np.zeros(3)
201
+ self.held = {1: None, -1: None}
202
+ self.stack = [] # world positions of the placed blocks
203
+ self.log = []
204
+ self.dbg = []
205
+ self.gen = self.main()
206
+ self.done = False
207
+
208
+ # ------------------------------------------------------------------ io
209
+ def act(self, obs, tools):
210
+ self.obs = obs
211
+ if not self.done:
212
+ try:
213
+ return next(self.gen)
214
+ except StopIteration:
215
+ self.done = True
216
+ except Exception as exc: # never crash the episode
217
+ self.log.append(('error', repr(exc)))
218
+ self.done = True
219
+ self.base_cmd = np.zeros(3)
220
+ try:
221
+ return self.compose()
222
+ except Exception:
223
+ return self.a.astype(np.float32).copy()
224
+
225
+ @property
226
+ def lo(self):
227
+ return np.asarray(self.obs['low_dim_obs'], dtype=np.float64)
228
+
229
+ def pose(self):
230
+ return self.lo[21:25]
231
+
232
+ def w2p(self, pw):
233
+ x, y, z, yaw = self.pose()
234
+ d = np.asarray(pw) - np.array([x, y, z])
235
+ c, s = np.cos(yaw), np.sin(yaw)
236
+ return np.array([c * d[0] + s * d[1], -s * d[0] + c * d[1], d[2]])
237
+
238
+ def p2w(self, pp):
239
+ x, y, z, yaw = self.pose()
240
+ c, s = np.cos(yaw), np.sin(yaw)
241
+ return np.array([x + c * pp[0] - s * pp[1], y + s * pp[0] + c * pp[1], z + pp[2]])
242
+
243
+ def hand_w(self, side):
244
+ lo = self.lo
245
+ q = lo[3:10] if side > 0 else lo[12:19]
246
+ return self.p2w(hand_pose(q, side, lo[0:3])[:3, 3])
247
+
248
+ def hand_p(self, side):
249
+ lo = self.lo
250
+ q = lo[3:10] if side > 0 else lo[12:19]
251
+ return hand_pose(q, side, lo[0:3])[:3, 3]
252
+
253
+ # ------------------------------------------------------------- control
254
+ def compose(self):
255
+ lo = self.lo
256
+ yaw = lo[24]
257
+ for side in (1, -1):
258
+ t = self.tgt[side]
259
+ if t is None:
260
+ continue
261
+ if self.frame[side] == 'world':
262
+ cur = self.hand_w(side)
263
+ tp_frame = t
264
+ else:
265
+ cur = self.hand_p(side)
266
+ tp_frame = t
267
+ err = tp_frame - cur
268
+ if self.frame[side] == 'world':
269
+ # slow integral term against gravity sag (fast gains oscillate)
270
+ self.corr[side] = np.clip(self.corr[side] + CORR_GAIN * err, -0.05, 0.05)
271
+ pc = tp_frame + self.corr[side]
272
+ if self.frame[side] == 'world':
273
+ pp = self.w2p(pc)
274
+ Rp = rz(-yaw) @ self.R[side]
275
+ else:
276
+ pp = pc
277
+ Rp = self.R[side]
278
+ self.q[side] = ik(pp, Rp, side, lo[0:3], self.q[side])
279
+ sl = slice(5, 12) if side > 0 else slice(12, 19)
280
+ self.a[sl] = self.q[side]
281
+ self.a[19] = self.grip[1]
282
+ self.a[20] = self.grip[-1]
283
+ self.a[0], self.a[1], self.a[3] = self.base_cmd
284
+ return self.a.astype(np.float32).copy()
285
+
286
+ def hold(self, n):
287
+ for _ in range(n):
288
+ yield self.compose()
289
+
290
+ def move(self, targets, n, frame=None):
291
+ """targets {side: point}; smooth interpolation in the side's frame."""
292
+ start = {}
293
+ for s, p in targets.items():
294
+ f = frame or self.frame[s]
295
+ if self.tgt[s] is None or f != self.frame[s]:
296
+ start[s] = self.hand_w(s) if f == 'world' else self.hand_p(s)
297
+ self.corr[s] = np.zeros(3)
298
+ else:
299
+ start[s] = self.tgt[s]
300
+ self.frame[s] = f
301
+ for k in range(n):
302
+ t = 0.5 - 0.5 * np.cos(np.pi * (k + 1) / n)
303
+ for s, p in targets.items():
304
+ self.tgt[s] = start[s] + (np.asarray(p, float) - start[s]) * t
305
+ yield self.compose()
306
+
307
+ def walk(self, x, y, yaw, maxn=900, tol=0.025, ytol=0.04, vmax=0.5, face=False):
308
+ """closed-loop walk to a world pose; with face=True turn toward the goal first."""
309
+ # arms keep their pose relative to the pelvis while walking
310
+ yaw0 = self.pose()[3]
311
+ for s in (1, -1):
312
+ if self.tgt[s] is not None and self.frame[s] == 'world':
313
+ self.tgt[s] = self.w2p(self.tgt[s])
314
+ self.R[s] = rz(-yaw0) @ self.R[s]
315
+ self.frame[s] = 'pelvis'
316
+ self.corr[s] = np.zeros(3)
317
+ best, best_i = 1e9, 0
318
+ for i in range(maxn):
319
+ px, py, _, pyaw = self.pose()
320
+ dx, dy = x - px, y - py
321
+ dist = np.hypot(dx, dy)
322
+ if dist < tol and abs(wrap(yaw - pyaw)) < ytol:
323
+ break
324
+ tyaw = yaw
325
+ if face and dist > 0.3:
326
+ tyaw = np.arctan2(dy, dx)
327
+ prog = dist + 0.3 * abs(wrap(tyaw - pyaw))
328
+ if prog < best - 0.01:
329
+ best, best_i = prog, i
330
+ elif i - best_i > 200:
331
+ self.log.append(('walk stalled', self.obs['t'], round(float(dist), 3)))
332
+ break
333
+ if face and dist > 0.3:
334
+ tyaw = np.arctan2(dy, dx)
335
+ c, s = np.cos(pyaw), np.sin(pyaw)
336
+ bx, by = c * dx + s * dy, -s * dx + c * dy
337
+ vx, vy = 1.5 * bx, 1.5 * by
338
+ n = np.hypot(vx, vy)
339
+ if n > vmax:
340
+ vx, vy = vx * vmax / n, vy * vmax / n
341
+ ey = wrap(tyaw - pyaw)
342
+ if face and dist > 0.3 and abs(ey) > 0.5:
343
+ vx, vy = 0.0, 0.0
344
+ if np.hypot(vx, vy) < 0.06:
345
+ if dist > tol:
346
+ k = 0.06 / max(np.hypot(vx, vy), 1e-6)
347
+ vx, vy = vx * k, vy * k
348
+ else:
349
+ vx = vy = 0.0
350
+ wz = np.clip(2.0 * ey, -0.8, 0.8)
351
+ if abs(wz) < 0.05:
352
+ wz = 0.0
353
+ self.base_cmd = np.array([vx, vy, wz])
354
+ yield self.compose()
355
+ self.base_cmd = np.zeros(3)
356
+ yield from self.hold(10)
357
+
358
+ # ------------------------------------------------------------- vision
359
+ def head_blobs(self, mask_fn, z, min_area=6):
360
+ im = self.tools.image('head')
361
+ lo = self.lo
362
+ out = []
363
+ for b in blobs(mask_fn(im), min_area):
364
+ p = pixel_to_plane(b['c'][0], b['c'][1], lo[0:3], z - lo[23])
365
+ b['w'] = self.p2w(p)
366
+ out.append(b)
367
+ return out
368
+
369
+ def wrist_block(self, side):
370
+ bl = blobs(red_mask(self.tools.image(CAMS[side])), 20)
371
+ return max(bl, key=lambda b: b['area']) if bl else None
372
+
373
+ def wrist_block_angle(self, side):
374
+ """angle (rad, in [-pi/4, pi/4)) of the block's top face edges in the wrist image."""
375
+ im = self.tools.image(CAMS[side])
376
+ r, g, b = [im[..., i].astype(int) for i in range(3)]
377
+ m = red_mask(im)
378
+ n, lab, st, cen = cv2.connectedComponentsWithStats(m)
379
+ if n < 2:
380
+ return None
381
+ i = 1 + int(np.argmax(st[1:, 4]))
382
+ if st[i, 4] < 30:
383
+ return None
384
+ ys, xs = np.nonzero(lab == i)
385
+ pts = np.stack([xs, ys], 1).astype(np.float32)
386
+ (cx, cy), (w, hh), ang = cv2.minAreaRect(pts)
387
+ a = np.radians(ang)
388
+ return (a + np.pi / 4) % (np.pi / 2) - np.pi / 4
389
+
390
+ # ------------------------------------------------------------- skills
391
+ def tuck(self, sides, n=50):
392
+ """carry pose in the pelvis frame, reached by joint interpolation to a canonical
393
+ IK solution so that later IK always starts from the same arm branch."""
394
+ lo = self.lo
395
+ goal, start, tg = {}, {}, {}
396
+ for s in sides:
397
+ tg[s] = np.array([0.38, 0.20 * s, 0.02])
398
+ self.R[s] = ry(0.6)
399
+ seed = np.array([-0.3, 0.3 * s, 0.0, 0.9, 0.0, 0.3, 0.0])
400
+ goal[s] = ik(tg[s], self.R[s], s, lo[0:3], seed, iters=150)
401
+ start[s] = self.q[s].copy()
402
+ self.tgt[s] = None
403
+ for k in range(n):
404
+ t = 0.5 - 0.5 * np.cos(np.pi * (k + 1) / n)
405
+ for s in sides:
406
+ self.q[s] = start[s] + (goal[s] - start[s]) * t
407
+ sl = slice(5, 12) if s > 0 else slice(12, 19)
408
+ self.a[sl] = self.q[s]
409
+ yield self.compose()
410
+ for s in sides:
411
+ self.tgt[s] = tg[s]
412
+ self.frame[s] = 'pelvis'
413
+ self.corr[s] = np.zeros(3)
414
+
415
+ def settle(self, s, tol=0.008, nmax=80):
416
+ """hold until the hand is within tol of its target."""
417
+ for k in range(nmax):
418
+ cur = self.hand_w(s) if self.frame[s] == 'world' else self.hand_p(s)
419
+ if np.linalg.norm(self.tgt[s] - cur) < tol and k >= 10:
420
+ break
421
+ yield self.compose()
422
+
423
+ def wrist_target(self, side, mask_fn=red_mask):
424
+ """blob in the wrist image closest to the reference position."""
425
+ bl = blobs(mask_fn(self.tools.image(CAMS[side])), 20)
426
+ if not bl:
427
+ return None
428
+ return min(bl, key=lambda b: np.hypot(b['c'][0] - REF_UV[0], b['c'][1] - REF_UV[1]))
429
+
430
+ def servo(self, s, P, yaw, iters=8, mask_fn=red_mask, gain=SERVO_GAIN):
431
+ """move the hand (pre-grasp height) until the wrist view matches REF_UV."""
432
+ c, sn = np.cos(yaw), np.sin(yaw)
433
+ for it in range(iters):
434
+ wb = self.wrist_target(s, mask_fn)
435
+ if wb is None:
436
+ break
437
+ du = REF_UV[0] - wb['c'][0]
438
+ dv = REF_UV[1] - wb['c'][1]
439
+ if abs(du) < 1.5 and abs(dv) < 1.5:
440
+ break
441
+ # hand +y -> block +u, hand +x -> block +v (hand frame, gripper yaw)
442
+ bx = np.clip(dv / gain, -0.03, 0.03)
443
+ by = np.clip(du / gain, -0.03, 0.03)
444
+ P = P + np.array([c * bx - sn * by, sn * bx + c * by, 0])
445
+ yield from self.move({s: P}, 20)
446
+ yield from self.hold(12)
447
+ return P
448
+
449
+ def locate(self, s, est, pad=False):
450
+ """precise FK-world centre of a block (or of the pad) seen by the free hand `s`."""
451
+ yaw = self.pose()[3]
452
+ self.R[s] = rz(yaw) @ ry(GTILT)
453
+ dz = PRE + (BLOCK / 2 if pad else 0.0)
454
+ P = np.array(est, float) + [0, 0, dz]
455
+ yield from self.move({s: P}, 60, frame='world')
456
+ yield from self.hold(15)
457
+ yield from self.servo(s, P, yaw, mask_fn=green_mask if pad else red_mask)
458
+ yield from self.hold(10)
459
+ c = self.hand_w(s) - [0, 0, dz]
460
+ if pad:
461
+ c = c + PAD_OFF
462
+ self.log.append(('locate', s, pad, np.round(est, 3).tolist(), c.round(3).tolist()))
463
+ return c
464
+
465
+ def grasp(self, side, B, pre=PRE):
466
+ """B: world estimate of the block centre; returns True if holding."""
467
+ s = side
468
+ yaw = self.pose()[3]
469
+ self.R[s] = rz(yaw) @ ry(GTILT)
470
+ self.grip[s] = 0.0
471
+ P = np.array(B, float) + [0, 0, pre]
472
+ yield from self.move({s: P}, 60, frame='world')
473
+ yield from self.hold(15)
474
+ if self.wrist_target(s) is None:
475
+ # block not in view: look from higher up (wider view), centre coarsely
476
+ P = P + [0, 0, 0.06]
477
+ yield from self.move({s: P}, 30)
478
+ yield from self.hold(10)
479
+ P = yield from self.servo(s, P, yaw, iters=4, gain=0.6 * SERVO_GAIN)
480
+ P = P - [0, 0, 0.06]
481
+ yield from self.move({s: P}, 30)
482
+ yield from self.hold(10)
483
+ P = yield from self.servo(s, P, yaw)
484
+ ang = self.wrist_block_angle(s)
485
+ self.log.append(('angle', s, None if ang is None else round(float(ang), 3)))
486
+ if ang is not None and abs(ang) > 0.12:
487
+ # turn the gripper onto the block faces; each wrist turns away from its
488
+ # yaw joint limit (the block is symmetric under 90 deg turns)
489
+ psi = -ang
490
+ if psi * s > 0.15:
491
+ psi -= s * np.pi / 2
492
+ for k in range(50):
493
+ self.R[s] = rz(yaw + psi * (k + 1) / 50) @ ry(GTILT)
494
+ yield self.compose()
495
+ yield from self.hold(15)
496
+ P = yield from self.servo(s, P, yaw + psi)
497
+ if DEBUG:
498
+ wb = self.wrist_block(s)
499
+ self.dbg.append(('pre', s, None if wb is None else (wb['c'].round(1).tolist(), wb['area']),
500
+ (self.hand_w(s) - P).round(3).tolist(),
501
+ self.tools.image(CAMS[s]), self.tools.image('head')))
502
+ # descend from where the hand actually is when the view matched the reference
503
+ G = self.hand_w(s) - [0, 0, pre]
504
+ yield from self.move({s: G}, 40)
505
+ yield from self.hold(10)
506
+ self.grip[s] = 1.0
507
+ yield from self.hold(30)
508
+ lo = self.lo
509
+ f = lo[10:12] if s > 0 else lo[19:21]
510
+ self.log.append(('close', s, f.round(3).tolist()))
511
+ if DEBUG:
512
+ self.dbg.append(('close', s, f.round(3).tolist(), (self.hand_w(s) - G).round(3).tolist(),
513
+ self.tools.image(CAMS[s]), self.tools.image('head')))
514
+ yield from self.move({s: G + [0, 0, 0.06]}, 40)
515
+ yield from self.hold(10)
516
+ lo = self.lo
517
+ f = lo[10:12] if s > 0 else lo[19:21]
518
+ wb = self.wrist_block(s)
519
+ area = 0 if wb is None else wb['area']
520
+ ok = bool(np.all(f > -0.017)) and area > 1200
521
+ self.log.append(('grasp', self.obs['t'], s, f.round(3).tolist(), area, ok))
522
+ self.held[s] = ok
523
+ return ok
524
+
525
+ def find_blocks(self, zc=None):
526
+ """blocks on a table (blobs of blocks held in a hand are dropped)."""
527
+ zc = Z_TABLE + BLOCK / 2 if zc is None else zc
528
+ bl = self.head_blobs(red_mask, zc)
529
+ lo = self.lo
530
+ hp = [project(self.hand_p(s), lo[0:3]) for s in (1, -1) if self.held[s]]
531
+ out = []
532
+ for b in bl:
533
+ if any(np.hypot(*(b['c'] - p)) < 10 for p in hp):
534
+ continue
535
+ out.append(b['w'])
536
+ return out
537
+
538
+ def pick(self, side, B):
539
+ """walk in front of block estimate B and grasp it with `side`."""
540
+ for attempt in range(3):
541
+ yield from self.walk(B[0] - REACH, B[1] - side * SIDE_OFF, 0.0)
542
+ yield from self.hold(10)
543
+ cand = self.find_blocks()
544
+ if cand:
545
+ B = min(cand, key=lambda b: np.linalg.norm(b[:2] - B[:2]))
546
+ ok = yield from self.grasp(side, B)
547
+ if ok:
548
+ break
549
+ self.grip[side] = 0.0
550
+ yield from self.move({side: B + [-0.12, 0, 0.12]}, 30)
551
+ yield from self.tuck([side])
552
+ return ok
553
+
554
+ def to_table2(self):
555
+ """table 1 (facing +x) -> viewing pose in front of table 2 (facing -x)."""
556
+ yield from self.walk(self.pose()[0] - 0.25, self.pose()[1], 0.0)
557
+ yield from self.walk(-0.25, 1.3, 0.0, face=True, tol=0.08, ytol=7)
558
+ yield from self.walk(2.6, 1.3, 0.0, face=True, tol=0.08, ytol=7)
559
+ yield from self.walk(2.6, 0.0, np.pi, face=True, tol=0.05, ytol=7)
560
+ yield from self.walk(2.35, 0.0, np.pi)
561
+
562
+ def to_table1(self):
563
+ yield from self.walk(self.pose()[0] + 0.25, self.pose()[1], np.pi)
564
+ yield from self.walk(2.6, 1.3, np.pi, face=True, tol=0.08, ytol=7)
565
+ yield from self.walk(-0.25, 1.3, np.pi, face=True, tol=0.08, ytol=7)
566
+ yield from self.walk(-0.25, 0.0, 0.0, face=True, tol=0.05, ytol=7)
567
+ yield from self.walk(0.05, 0.0, 0.0)
568
+
569
+ def find_pad(self):
570
+ bl = self.head_blobs(green_mask, Z_TABLE, min_area=4)
571
+ if not bl:
572
+ return None
573
+ b = max(bl, key=lambda b: b['area'])
574
+ return b['w']
575
+
576
+ def place(self, side, C):
577
+ """lower the block held by `side` until its centre is at world point C, release."""
578
+ s = side
579
+ yaw = self.pose()[3]
580
+ self.R[s] = rz(yaw) @ ry(TILT)
581
+ C = np.array(C, float)
582
+ above = C + [0, 0, 0.07]
583
+ yield from self.move({s: above}, 60, frame='world')
584
+ yield from self.settle(s)
585
+ yield from self.move({s: C + [0, 0, 0.008]}, 40)
586
+ yield from self.settle(s, nmax=60)
587
+ top = self.hand_w(s)
588
+ self.grip[s] = 0.0
589
+ yield from self.hold(25)
590
+ self.tgt[s] = self.hand_w(s)
591
+ self.corr[s] = np.zeros(3)
592
+ yield from self.move({s: self.tgt[s] + [0, 0, 0.08]}, 40)
593
+ self.held[s] = None
594
+ self.log.append(('placed', self.obs['t'], s, C.round(3).tolist(), top.round(3).tolist()))
595
+ return top
596
+
597
+ def stand_for(self, side, target, yaw):
598
+ """walk so that world xy `target` sits at (REACH, side*SIDE_OFF) in the pelvis frame."""
599
+ c, s_ = np.cos(yaw), np.sin(yaw)
600
+ rel = np.array([REACH, side * SIDE_OFF])
601
+ bx = target[0] - (c * rel[0] - s_ * rel[1])
602
+ by = target[1] - (s_ * rel[0] + c * rel[1])
603
+ yield from self.walk(bx, by, yaw)
604
+ yield from self.hold(15)
605
+
606
+ def stand_at(self, C, yaw, lateral=0.0):
607
+ """walk so that world xy C sits at (REACH, lateral) in the pelvis frame."""
608
+ c, s_ = np.cos(yaw), np.sin(yaw)
609
+ bx = C[0] - (c * REACH - s_ * lateral)
610
+ by = C[1] - (s_ * REACH + c * lateral)
611
+ yield from self.walk(bx, by, yaw)
612
+ yield from self.hold(15)
613
+
614
+ def view_pad(self):
615
+ """from the viewing pose, find the pad and stand in front of it (facing -x)."""
616
+ pad = self.find_pad()
617
+ if pad is None:
618
+ for dy in (0.4, -0.4):
619
+ yield from self.walk(2.35, dy, np.pi)
620
+ pad = self.find_pad()
621
+ if pad is not None:
622
+ break
623
+ if pad is None:
624
+ pad = np.array([1.55, 0.0, Z_TABLE])
625
+ yield from self.stand_at(pad, np.pi)
626
+ p2 = self.find_pad()
627
+ if p2 is not None:
628
+ pad = p2
629
+ self.log.append(('pad', self.obs['t'], pad.round(3).tolist()))
630
+ return pad
631
+
632
+ def stack_trip1(self):
633
+ """both hands hold a block; robot stands with the pad at (REACH, 0)."""
634
+ pad = yield from self.view_pad()
635
+ PC = np.array([pad[0], pad[1], Z_TABLE + BLOCK / 2])
636
+ # each hand works on its own side: pad in front of the left hand
637
+ yield from self.stand_at(PC, np.pi, lateral=SIDE_OFF)
638
+ # a. right block set down in front of the right hand, right arm out of the way
639
+ spot = self.p2w(np.array([REACH, -SIDE_OFF, 0.0]))
640
+ spot[2] = Z_TABLE + BLOCK / 2
641
+ spot = yield from self.place(-1, spot)
642
+ yield from self.tuck([-1])
643
+ # b. left block onto the pad; the hand position at release is where it sits
644
+ B1 = yield from self.place(1, PC)
645
+ yield from self.tuck([1])
646
+ # c. right hand picks the set-down block up again
647
+ ok = yield from self.grasp(-1, spot)
648
+ if ok:
649
+ yield from self.tuck([-1])
650
+ # d. step sideways so the stack is in front of the right hand
651
+ yield from self.stand_at(B1, np.pi, lateral=-SIDE_OFF)
652
+ yield from self.place(-1, B1 + [0, 0, BLOCK])
653
+ yield from self.tuck([-1])
654
+ return B1
655
+
656
+ def main(self):
657
+ self.a[4] = PITCH
658
+ yield from self.hold(40)
659
+ blocks = self.find_blocks()
660
+ self.log.append(('blocks', [b.round(3).tolist() for b in blocks]))
661
+ blocks = sorted(blocks, key=lambda b: -b[1])
662
+ if len(blocks) < 2:
663
+ return
664
+ yield from self.pick(-1, blocks[-1])
665
+ yield from self.pick(1, blocks[0])
666
+ self.rest = blocks[1:-1]
667
+ yield from self.to_table2()
668
+ B1 = yield from self.stack_trip1()
669
+ self.B1 = B1
670
+ yield from self.trip2(B1)
671
+
672
+ def trip2(self, B1):
673
+ s = -1
674
+ yield from self.to_table1()
675
+ cand = self.find_blocks()
676
+ if not cand:
677
+ return
678
+ B = min(cand, key=lambda b: abs(b[1]))
679
+ ok = yield from self.pick(s, B)
680
+ if not ok:
681
+ return
682
+ yield from self.to_table2()
683
+ yield from self.stand_at(B1, np.pi, lateral=s * SIDE_OFF)
684
+ yield from self.place(s, B1 + [0, 0, 2 * BLOCK])
685
+ yield from self.tuck([s])
686
+ yield from self.hold(100)
policies/stack_blocks/opus-s3/cam.py ADDED
@@ -0,0 +1,51 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Head camera model (fitted from robot-motion observations of static blocks)."""
2
+ import numpy as np
3
+ import fk
4
+
5
+ CAM = np.array([0.0879, 0.0304, 0.3378, 0.8672, 0.0061, 0.0083, 66.0443])
6
+ # head camera at the initial state (t=0), fitted to wrist-measured block positions
7
+ CAM0 = np.array([0.0862, 0.0096, 0.3415, 0.9988, -0.0457, -0.045, 74.1238])
8
+ PEL0 = np.array([0.109, -0.003, 0.747, 0.001])
9
+ WAIST0 = np.array([0.001, -0.009, 0.053])
10
+ # wrist cameras in the hand (wrist-yaw) frame: x, y, z, roll, pitch, yaw, f
11
+ WCAM = {
12
+ 'left': np.array([0.1158, -0.0003, 0.0244, 0.0089, 0.6207, 0.0309, 70.3616]),
13
+ 'right': np.array([0.1158, 0.0003, 0.0244, -0.0089, 0.6207, -0.0309, 70.3616]), # mirror of left
14
+ }
15
+
16
+
17
+ def wrist_offset(side):
18
+ c = WCAM[side]
19
+ return fk.T(fk.rpy(c[3], c[4], c[5]), c[0:3])
20
+
21
+
22
+ def wrist_point(side, depth):
23
+ """hand-frame point at `depth` along the wrist camera axis."""
24
+ M = wrist_offset(side)
25
+ return M[:3, 3] + depth * M[:3, 0]
26
+
27
+
28
+ def wrist_ray(side, Thand, uv):
29
+ """origin and direction (frame of Thand) of the ray through pixel uv."""
30
+ c = WCAM[side]
31
+ Tc = Thand @ wrist_offset(side)
32
+ d = np.array([c[6], 42 - uv[0], 42 - uv[1]])
33
+ return Tc[:3, 3], Tc[:3, :3] @ d / np.linalg.norm(d)
34
+
35
+
36
+ def head_T(pel, waist, ppitch=0.0, c=CAM):
37
+ Wp = fk.T(fk.rot('z', pel[3]) @ fk.rot('y', ppitch), (pel[0], pel[1], pel[2]))
38
+ return Wp @ fk.torso_T(waist) @ fk.T(fk.rpy(c[5], c[3], c[4]), c[0:3])
39
+
40
+
41
+ def project(Tc, P, f=CAM[6]):
42
+ d = Tc[:3, :3].T @ (np.asarray(P) - Tc[:3, 3])
43
+ return np.array([42 - f * d[1] / d[0], 42 - f * d[2] / d[0]])
44
+
45
+
46
+ def ray_plane(Tc, uv, z, f=CAM[6]):
47
+ """World point where the pixel ray hits the horizontal plane at height z."""
48
+ d = np.array([f, 42 - uv[0], 42 - uv[1]])
49
+ dw = Tc[:3, :3] @ d
50
+ t = (z - Tc[2, 3]) / dw[2]
51
+ return Tc[:3, 3] + t * dw
policies/stack_blocks/opus-s3/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ seed,success,length,termination,fell
2
+ 620000,0,8750,timeout,0
3
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4
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5
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6
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7
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8
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9
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10
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11
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12
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13
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14
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15
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17
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18
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20
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21
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22
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25
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26
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27
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28
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29
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30
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31
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34
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35
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36
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38
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40
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44
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45
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47
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50
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55
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56
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59
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60
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62
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63
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65
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66
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67
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68
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69
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70
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71
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72
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73
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74
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76
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77
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79
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80
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83
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85
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86
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88
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89
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90
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92
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96
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100
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101
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policies/stack_blocks/opus-s3/fk.py ADDED
@@ -0,0 +1,132 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Forward kinematics of the G1 upper body (URDF geometry), pelvis frame."""
2
+ import numpy as np
3
+
4
+
5
+ def rot(axis, a):
6
+ c, s = np.cos(a), np.sin(a)
7
+ if axis == 'x':
8
+ return np.array([[1, 0, 0], [0, c, -s], [0, s, c]])
9
+ if axis == 'y':
10
+ return np.array([[c, 0, s], [0, 1, 0], [-s, 0, c]])
11
+ return np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]])
12
+
13
+
14
+ def rpy(r, p, y):
15
+ return rot('z', y) @ rot('y', p) @ rot('x', r)
16
+
17
+
18
+ def T(R=None, p=(0, 0, 0)):
19
+ M = np.eye(4)
20
+ if R is not None:
21
+ M[:3, :3] = R
22
+ M[:3, 3] = p
23
+ return M
24
+
25
+
26
+ def torso_T(waist):
27
+ """pelvis -> torso_link given waist (yaw, roll, pitch)."""
28
+ M = T(rot('z', waist[0]))
29
+ M = M @ T(rot('x', waist[1]), (-0.0039635, 0, 0.035))
30
+ M = M @ T(rot('y', waist[2]), (0, 0, 0.019))
31
+ return M
32
+
33
+
34
+ # (origin xyz, origin rpy, axis) for the left arm; right arm mirrors y.
35
+ LEFT_CHAIN = [
36
+ ((0.0039563, 0.10022, 0.23778), (0.27931, 5.4949e-05, -0.00019159), 'y'),
37
+ ((0, 0.038, -0.013831), (-0.27925, 0, 0), 'x'),
38
+ ((0, 0.00624, -0.1032), (0, 0, 0), 'z'),
39
+ ((0.015783, 0, -0.080518), (0, 0, 0), 'y'),
40
+ ((0.100, 0.00188791, -0.010), (0, 0, 0), 'x'),
41
+ ((0.038, 0, 0), (0, 0, 0), 'y'),
42
+ ((0.046, 0, 0), (0, 0, 0), 'z'),
43
+ ]
44
+
45
+
46
+ def arm_chain(side):
47
+ if side == 'left':
48
+ return LEFT_CHAIN
49
+ out = []
50
+ for (x, y, z), (r, p, yw), ax in LEFT_CHAIN:
51
+ out.append(((x, -y, z), (-r, p, -yw), ax))
52
+ return out
53
+
54
+
55
+ def arm_T(side, q, waist=(0, 0, 0), tool=(0.0, 0, 0)):
56
+ """pelvis -> hand frame (after wrist yaw, translated by tool offset)."""
57
+ M = torso_T(waist)
58
+ for (p, r, ax), qi in zip(arm_chain(side), q):
59
+ M = M @ T(rpy(*r), p) @ T(rot(ax, qi))
60
+ return M @ T(None, tool)
61
+
62
+
63
+ # joint box used by the IK: the action limits narrowed to postures that keep the
64
+ # arm on its own side of the body (left arm; the right arm mirrors roll/yaw)
65
+ SANE_LO = np.array([-2.6, 0.0, -1.3, -1.0, -1.6, -1.55, -1.55])
66
+ SANE_HI = np.array([1.0, 1.6, 1.3, 2.0, 1.6, 1.55, 1.55])
67
+ Q_NOM = [np.array([-0.4, 0.15, 0.0, 0.9, 0.0, 0.4, 0.0]),
68
+ np.array([-0.9, 0.2, 0.0, 0.4, 0.0, 0.9, 0.0]),
69
+ np.array([0.0, 0.15, 0.0, 1.2, 0.0, 0.0, 0.0])]
70
+
71
+
72
+ def limits(side):
73
+ lo, hi = SANE_LO.copy(), SANE_HI.copy()
74
+ if side == 'right':
75
+ for i in (1, 2, 4, 6):
76
+ lo[i], hi[i] = -SANE_HI[i], -SANE_LO[i]
77
+ return lo, hi
78
+
79
+
80
+ def mirror(q, side):
81
+ q = np.array(q, float)
82
+ if side == 'right':
83
+ q[[1, 2, 4, 6]] *= -1
84
+ return q
85
+
86
+
87
+ def _ik1(side, p, xdir, ydir, q0, waist, tool, w_ori, w_reg, lo, hi):
88
+ from scipy.optimize import least_squares
89
+ q0 = np.clip(np.asarray(q0, float), lo + 1e-3, hi - 1e-3)
90
+ qref = q0.copy()
91
+
92
+ def res(q):
93
+ M = arm_T(side, q, waist, tool)
94
+ r = [M[:3, 3] - p]
95
+ if xdir is not None:
96
+ r.append(w_ori * (M[:3, 0] - xdir))
97
+ if ydir is not None:
98
+ r.append(w_ori * (M[:3, 1] - ydir))
99
+ r.append(w_reg * (q - qref))
100
+ return np.concatenate(r)
101
+
102
+ sol = least_squares(res, q0, bounds=(lo, hi))
103
+ M = arm_T(side, sol.x, waist, tool)
104
+ e = np.linalg.norm(M[:3, 3] - p)
105
+ eo = 0.0
106
+ if xdir is not None:
107
+ eo += np.linalg.norm(M[:3, 0] - xdir)
108
+ if ydir is not None:
109
+ eo += np.linalg.norm(M[:3, 1] - ydir)
110
+ return sol.x, e, eo
111
+
112
+
113
+ def ik(side, p, xdir=None, ydir=None, q0=None, waist=(0, 0, 0), tool=(0.12, 0, 0),
114
+ w_ori=1.0, w_reg=0.01, multi=True):
115
+ """Least-squares IK: tool point to p, hand x axis to xdir, hand y axis to ydir.
116
+ Tries the given start and nominal postures; returns (q, position error)."""
117
+ lo, hi = limits(side)
118
+ p = np.asarray(p, float)
119
+ starts = [] if q0 is None else [np.asarray(q0, float)]
120
+ best = None
121
+ if starts:
122
+ q, e, eo = _ik1(side, p, xdir, ydir, starts[0], waist, tool, w_ori, w_reg, lo, hi)
123
+ best = (e + 0.1 * eo, q, e)
124
+ if e < 0.003 and eo < 0.03:
125
+ return q, e
126
+ if multi or not starts:
127
+ for qn in Q_NOM:
128
+ q, e, eo = _ik1(side, p, xdir, ydir, mirror(qn, side), waist, tool, w_ori, w_reg, lo, hi)
129
+ c = e + 0.1 * eo + 0.002
130
+ if best is None or c < best[0]:
131
+ best = (c, q, e)
132
+ return best[1], best[2]
policies/stack_blocks/opus-s3/policy.py ADDED
@@ -0,0 +1,718 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Stack-blocks policy for the G1: hand-written state machine.
2
+
3
+ Plan: grasp two blocks on table 1 (one per hand, wrist-camera visual servo),
4
+ walk around to table 2, put the first on the green pad and the second on top,
5
+ walk back for the third block and stack it.
6
+
7
+ Geometry: fk.py holds the G1 arm kinematics (URDF dimensions) and a bounded IK;
8
+ cam.py holds the head / wrist camera models, calibrated from the robot's own
9
+ camera views of static and held blocks. Blocks are located with the head camera
10
+ at t=0 and refined with a wrist camera; placements close the loop on the
11
+ forward-kinematic position of the held block in world coordinates.
12
+ """
13
+
14
+ import os
15
+ import sys
16
+
17
+ import numpy as np
18
+ import scipy.ndimage as ndi
19
+
20
+ sys.path.append(os.path.dirname(os.path.abspath(__file__)))
21
+ import fk # noqa: E402
22
+ import cam # noqa: E402
23
+
24
+ PELVIS_H = 0.74
25
+ BLOCK_Z = 0.718 # block centre height on a table (world)
26
+ TABLE_Z = 0.70 # table top (world)
27
+ BLOCK = 0.05 # block edge
28
+ CAM_PITCH = 0.68 # gripper / wrist camera axis, below the hand x axis (rad)
29
+ GRASP_OFF = (0.183, 0.0, -0.005) # grasp centre in the hand frame (m)
30
+ WRIST_F = 68.0 # wrist camera focal length (px)
31
+ ALPHA = 0.9 # gripper axis below horizontal while grasping (rad)
32
+ # wrist camera appearance of a block at the moment of closing (demo, left wrist)
33
+ WRIST_UV = np.array([43.0, 36.0])
34
+ WRIST_AREA = 5000.0
35
+
36
+ # route between the tables (world)
37
+ HELD_OFF = np.array([0.200, 0.0, 0.008]) # held block centre in the hand frame (measured)
38
+ PAD_SIZE = 0.11 # green pad edge (wrist-camera measurement)
39
+ PAD_CHECKS = 0 # wrist-camera pad re-check of block 1 (disabled: not validated)
40
+ PAD_TOL = 0.012 # re-place block 1 if its head-image offset from the pad exceeds this
41
+ PLACE_DZ = 0.006 # release height of the block bottom above its support
42
+ VIEW_ALPHA = 0.95
43
+ VIEW_DIST = 0.20
44
+ STAND_X_MAX = 0.20 # closest pelvis x in front of table 1 (edge at x=0.385)
45
+ PAD_DEFAULT = np.array([1.60, 0.0, TABLE_Z])
46
+ T2_DX = 0.40 # pelvis stands this far (+x) from the pad, facing -x
47
+ T2_STAND = np.array([1.95, 0.0, np.pi])
48
+ VIA = [np.array([-0.05, 1.05]), np.array([2.45, 1.05]), np.array([2.45, 0.0])]
49
+
50
+ ARM_REST = {
51
+ 'left': np.array([0.0, 0.15, 0.0, 1.5, 0.0, 0.0, 0.0]),
52
+ 'right': np.array([0.0, -0.15, 0.0, 1.5, 0.0, 0.0, 0.0]),
53
+ }
54
+
55
+
56
+ def wrap(a):
57
+ return (a + np.pi) % (2 * np.pi) - np.pi
58
+
59
+
60
+ def red_mask(im):
61
+ r, g, b = [im[..., i].astype(int) for i in range(3)]
62
+ return (r > 100) & (g < 70) & (b < 70) & (r > 2 * g)
63
+
64
+
65
+ def green_mask(im):
66
+ r, g, b = [im[..., i].astype(int) for i in range(3)]
67
+ return (g > 150) & (g > r + 40) & (g > b + 40)
68
+
69
+
70
+ def components(mask, min_px=8):
71
+ lab, k = ndi.label(mask)
72
+ out = []
73
+ for i in range(1, k + 1):
74
+ ys, xs = np.nonzero(lab == i)
75
+ if len(xs) >= min_px:
76
+ out.append(dict(u=xs.mean(), v=ys.mean(), n=len(xs),
77
+ box=(xs.min(), xs.max(), ys.min(), ys.max())))
78
+ return out
79
+
80
+
81
+ class Policy:
82
+ """The control policy that is scored on the hidden seeds."""
83
+
84
+ def reset(self, obs, tools):
85
+ self.tools = tools
86
+ self.obs = obs
87
+ self.A = np.zeros(21)
88
+ self.A[2] = PELVIS_H
89
+ o = obs['low_dim_obs']
90
+ self.A[5:12] = o[3:10]
91
+ self.A[12:19] = o[12:19]
92
+ self.A[19] = self.A[20] = 0.0
93
+ self.q = {'left': o[3:10].astype(float).copy(), 'right': o[12:19].astype(float).copy()}
94
+ self.held = {'left': False, 'right': False}
95
+ self.grip_pose = {'left': (0.0, 1.0), 'right': (0.0, 1.0)}
96
+ self.stack = [] # world positions of placed blocks
97
+ self.log = []
98
+ self.phase = 'start'
99
+ self.gen = self.script()
100
+
101
+ # ------------------------------------------------------------------ utils
102
+ @property
103
+ def o(self):
104
+ return self.obs['low_dim_obs']
105
+
106
+ def note(self, *a):
107
+ self.log.append((int(self.obs['t']), self.phase) + a)
108
+
109
+ def w2p(self, P):
110
+ """world point -> pelvis yaw frame (z relative to pelvis)."""
111
+ o = self.o
112
+ d = np.asarray(P, float) - o[21:24]
113
+ c, s = np.cos(o[24]), np.sin(o[24])
114
+ return np.array([c * d[0] + s * d[1], -s * d[0] + c * d[1], d[2]])
115
+
116
+ def p2w(self, p):
117
+ o = self.o
118
+ c, s = np.cos(o[24]), np.sin(o[24])
119
+ return np.array([o[21] + c * p[0] - s * p[1], o[22] + s * p[0] + c * p[1], o[23] + p[2]])
120
+
121
+ def act(self, obs, tools):
122
+ self.obs = obs
123
+ try:
124
+ a = next(self.gen)
125
+ except StopIteration:
126
+ a = None
127
+ except Exception:
128
+ # never crash an episode: freeze in place
129
+ self.gen = iter(())
130
+ a = None
131
+ if a is None:
132
+ self.A[0] = self.A[1] = self.A[3] = 0.0
133
+ a = self.A
134
+ return np.asarray(a, dtype=np.float32).copy()
135
+
136
+ def wait(self, n):
137
+ for _ in range(n):
138
+ yield self.A
139
+
140
+ # ------------------------------------------------------------- base motion
141
+ def walk_to(self, x, y, yaw=None, tol=0.03, ytol=0.05, n=1500, speed=0.4, final=True):
142
+ A = self.A
143
+ for i in range(n):
144
+ o = self.o
145
+ px, py, pyaw = o[21], o[22], o[24]
146
+ dx, dy = x - px, y - py
147
+ dist = np.hypot(dx, dy)
148
+ c, s = np.cos(pyaw), np.sin(pyaw)
149
+ fx, fy = c * dx + s * dy, -s * dx + c * dy
150
+ if dist > 0.25 or (not final and dist > 0.12):
151
+ hd = wrap(np.arctan2(dy, dx) - pyaw)
152
+ if yaw is not None and dist < 0.6:
153
+ # close to the goal: keep the final heading, side-step
154
+ hd2 = wrap(yaw - pyaw)
155
+ wz = np.clip(1.5 * hd2, -0.6, 0.6)
156
+ vx, vy = np.clip(1.2 * fx, -speed, speed), np.clip(1.2 * fy, -0.3, 0.3)
157
+ else:
158
+ wz = np.clip(1.5 * hd, -0.8, 0.8)
159
+ vx = speed * max(0.0, np.cos(hd)) ** 4 if abs(hd) < 0.6 else 0.0
160
+ vx = min(vx, 0.6 * dist + 0.1)
161
+ vy = np.clip(1.0 * fy, -0.2, 0.2) if abs(hd) < 0.6 else 0.0
162
+ else:
163
+ if not final:
164
+ return
165
+ yf = yaw if yaw is not None else pyaw
166
+ hd = wrap(yf - pyaw)
167
+ wz = np.clip(1.5 * hd, -0.6, 0.6)
168
+ vx, vy = np.clip(1.5 * fx, -0.2, 0.2), np.clip(1.5 * fy, -0.2, 0.2)
169
+ if abs(fx) < tol and abs(fy) < tol and abs(hd) < ytol:
170
+ break
171
+ sp = np.hypot(vx, vy)
172
+ if 0 < sp < 0.065:
173
+ vx, vy = vx / sp * 0.065, vy / sp * 0.065
174
+ if abs(wz) < 0.06 and abs(wz) > 0.005 and sp < 0.065:
175
+ wz = 0.06 * np.sign(wz)
176
+ A[0], A[1], A[3] = vx, vy, wz
177
+ yield A
178
+ A[0] = A[1] = A[3] = 0.0
179
+ yield from self.wait(25)
180
+
181
+ # -------------------------------------------------------------- arm motion
182
+ def arm_to(self, side, q, n=40, grip=None):
183
+ """linear joint interpolation to q over n steps."""
184
+ sl = slice(5, 12) if side == 'left' else slice(12, 19)
185
+ q0 = self.A[sl].copy()
186
+ q = np.asarray(q, float)
187
+ for i in range(1, n + 1):
188
+ self.A[sl] = q0 + (q - q0) * i / n
189
+ if grip is not None:
190
+ self.A[19 if side == 'left' else 20] = grip
191
+ yield self.A
192
+ self.q[side] = q.copy()
193
+
194
+ def grip(self, side, g, n=30):
195
+ self.A[19 if side == 'left' else 20] = g
196
+ yield from self.wait(n)
197
+
198
+ def azimuth(self, side, p):
199
+ sh = np.array([0.0, 0.10 if side == 'left' else -0.10])
200
+ yaw = np.arctan2(p[1] - sh[1], max(p[0] - sh[0], 0.05))
201
+ return float(np.clip(yaw, -0.8, 0.8))
202
+
203
+ @staticmethod
204
+ def frame(psi, alpha):
205
+ """gripper axis a (azimuth psi, alpha below horizontal), closing axis y,
206
+ camera-up n, and the hand x/y axes that realise it."""
207
+ a = np.array([np.cos(alpha) * np.cos(psi), np.cos(alpha) * np.sin(psi), -np.sin(alpha)])
208
+ y = np.array([-np.sin(psi), np.cos(psi), 0.0])
209
+ n = np.cross(a, y)
210
+ x = np.cos(CAM_PITCH) * a + np.sin(CAM_PITCH) * n
211
+ return a, y, n, x
212
+
213
+ def solve(self, side, p, xd, yd, tool=GRASP_OFF, q0=None, multi=True):
214
+ q0 = self.q[side] if q0 is None else q0
215
+ q, err = fk.ik(side, np.asarray(p, float), xdir=xd, ydir=yd, q0=q0,
216
+ waist=self.o[0:3], tool=tool, multi=multi)
217
+ return q, err
218
+
219
+ def hand_pose(self, side, tool=GRASP_OFF):
220
+ o = self.o
221
+ q = o[3:10] if side == 'left' else o[12:19]
222
+ return fk.arm_T(side, q, o[0:3], tool)
223
+
224
+ # -------------------------------------------------------------- perception
225
+ def head_blocks(self, ppitch=0.0):
226
+ im = self.tools.image('head')
227
+ o = self.o
228
+ Tc = cam.head_T(o[21:25], o[0:3], ppitch)
229
+ out = []
230
+ for c in components(red_mask(im), 15):
231
+ P = cam.ray_plane(Tc, (c['u'], c['v']), BLOCK_Z)
232
+ out.append(dict(c, P=P))
233
+ return out
234
+
235
+ def head_pad(self, ppitch=0.0):
236
+ im = self.tools.image('head')
237
+ o = self.o
238
+ Tc = cam.head_T(o[21:25], o[0:3], ppitch)
239
+ cs = components(green_mask(im), 10)
240
+ if not cs:
241
+ return None
242
+ c = max(cs, key=lambda c: c['n'])
243
+ return dict(c, P=cam.ray_plane(Tc, (c['u'], c['v']), TABLE_Z))
244
+
245
+ def wrist_blob(self, side):
246
+ im = self.tools.image('left_wrist' if side == 'left' else 'right_wrist')
247
+ cs = components(red_mask(im), 20)
248
+ if not cs:
249
+ return None
250
+ return max(cs, key=lambda c: c['n'])
251
+
252
+ # ------------------------------------------------------------------ grasp
253
+ def set_arm(self, side, q):
254
+ sl = slice(5, 12) if side == 'left' else slice(12, 19)
255
+ self.A[sl] = q
256
+ self.q[side] = np.asarray(q, float).copy()
257
+
258
+ @staticmethod
259
+ def grasp_point(side):
260
+ return tuple(cam.wrist_point(side, 0.05 * cam.WCAM[side][6] / 62.0))
261
+
262
+ def wrist_locate(self, side, near=None, zw=BLOCK_Z, color='red'):
263
+ """blobs seen by a wrist camera -> pelvis-frame positions on plane zw."""
264
+ im = self.tools.image(side + '_wrist')
265
+ mask = red_mask(im) if color == 'red' else green_mask(im)
266
+ lab, k = ndi.label(mask)
267
+ o = self.o
268
+ Th = fk.arm_T(side, o[3:10] if side == 'left' else o[12:19], o[0:3])
269
+ zp = zw - o[23]
270
+ out = []
271
+ for i in range(1, k + 1):
272
+ ys, xs = np.nonzero(lab == i)
273
+ if len(xs) < 30:
274
+ continue
275
+ org, d = cam.wrist_ray(side, Th, (xs.mean(), ys.mean()))
276
+ if d[2] > -0.05:
277
+ continue
278
+ P = org + d * (zp - org[2]) / d[2]
279
+ # yaw of the top face: project pixels onto the top plane, min-area rectangle
280
+ yaw = None
281
+ try:
282
+ import cv2
283
+ c = cam.WCAM[side]
284
+ Tc = Th @ cam.wrist_offset(side)
285
+ D = np.stack([np.full(len(xs), c[6]), 42.0 - xs, 42.0 - ys], 1) @ Tc[:3, :3].T
286
+ zt = zp + BLOCK / 2
287
+ t = (zt - Tc[2, 3]) / D[:, 2]
288
+ pts = (Tc[:3, 3][None, :2] + D[:, :2] * t[:, None]).astype(np.float32) * 1000
289
+ (cx, cy), (w, h), ang = cv2.minAreaRect(pts)
290
+ yaw = np.radians(ang) % (np.pi / 2)
291
+ except Exception:
292
+ pass
293
+ out.append(dict(P=P, n=len(xs), u=xs.mean(), v=ys.mean(), yaw=yaw,
294
+ box=(xs.min(), xs.max(), ys.min(), ys.max())))
295
+ if near is not None and out:
296
+ out.sort(key=lambda b: np.hypot(*(b['P'][:2] - near[:2])))
297
+ return out
298
+
299
+ def grasp(self, side, Pw, alpha=1.0):
300
+ """Pick the block near world point Pw with one hand."""
301
+ self.phase = 'grasp_' + side
302
+ p = self.w2p(Pw)
303
+ psi = self.azimuth(side, p)
304
+ a, y, n, x = self.frame(psi, VIEW_ALPHA)
305
+ tv = tuple(cam.wrist_point(side, VIEW_DIST))
306
+ q, err = self.solve(side, p + [0, 0, 0.10], x, y, tool=tv)
307
+ yield from self.arm_to(side, q, 40, grip=0.0)
308
+ q, err = self.solve(side, p, x, y, tool=tv, q0=q)
309
+ yield from self.arm_to(side, q, 25, grip=0.0)
310
+ yield from self.wait(12)
311
+ seen = self.wrist_locate(side, near=p)
312
+ if seen and np.hypot(*(seen[0]['P'][:2] - p[:2])) < 0.12:
313
+ b = seen[0]
314
+ p = b['P']
315
+ self.note('view', np.round(p, 3), b['n'], None if b['yaw'] is None else round(float(b['yaw']), 3))
316
+ yaw = b['yaw']
317
+ else:
318
+ self.note('view-miss', len(seen))
319
+ yaw = None
320
+ psi0 = self.azimuth(side, p)
321
+ cands = [psi0]
322
+ if yaw is not None:
323
+ # closing axis perpendicular to a pair of faces
324
+ cands = [yaw + k * np.pi / 2 for k in range(-3, 4)]
325
+ cands = [c for c in cands if abs(wrap(c - psi0)) < 0.9]
326
+ tg = self.grasp_point(side)
327
+ best = None
328
+ for psi in cands:
329
+ for al in (1.0, 1.2, 0.8):
330
+ a, y, n, x = self.frame(psi, al)
331
+ q1, e1 = self.solve(side, p - 0.09 * a, x, y, tool=tg)
332
+ q2, e2 = self.solve(side, p, x, y, tool=tg, q0=q1)
333
+ cost = e1 + 3 * e2 + 0.01 * abs(wrap(psi - psi0))
334
+ if best is None or cost < best[0]:
335
+ best = (cost, psi, al, q1, q2, e2)
336
+ _, psi, al, q1, q2, e2 = best
337
+ self.note('grasp-pose', round(float(psi), 3), al, round(float(e2), 4))
338
+ a, y, n, x = self.frame(psi, al)
339
+ yield from self.arm_to(side, q1, 30, grip=0.0)
340
+ tgt = p - 0.09 * a
341
+ q = q1
342
+ f = cam.WCAM[side][6]
343
+ best_n, stall = 0, 0
344
+ for it in range(40):
345
+ yield from self.wait(4)
346
+ b = self.wrist_blob(side)
347
+ if b is None:
348
+ self.note('servo-lost', it)
349
+ break
350
+ eu, ev = b['u'] - WRIST_UV[0], b['v'] - WRIST_UV[1]
351
+ depth = 0.052 * np.sqrt(WRIST_AREA / max(b['n'], 50))
352
+ if b['n'] > 0.9 * WRIST_AREA and abs(eu) < 6 and abs(ev) < 7:
353
+ break
354
+ if b['n'] > best_n:
355
+ best_n, stall = b['n'], 0
356
+ else:
357
+ stall += 1
358
+ if best_n > 0.75 * WRIST_AREA and stall >= 3 and abs(eu) < 14 and abs(ev) < 14:
359
+ break
360
+ k = 0.6 * min(depth, 0.15) / f
361
+ fwd = float(np.clip(0.5 * (depth - 0.052), 0.0, 0.012)) if abs(eu) < 12 and abs(ev) < 12 else 0.0
362
+ tgt = tgt + fwd * a - k * eu * y - k * ev * n
363
+ q, err = self.solve(side, tgt, x, y, tool=tg, q0=q, multi=False)
364
+ self.set_arm(side, q)
365
+ self.note('servo', it, round(b['u'], 1), round(b['v'], 1), b['n'], round(err, 3))
366
+ yield from self.wait(4)
367
+ b = self.wrist_blob(side)
368
+ self.note('pregrip', None if b is None else (round(b['u'], 1), round(b['v'], 1), b['n']), np.round(tgt - p, 3))
369
+ yield from self.grip(side, 1.0, 30)
370
+ g = self.o[50 if side == 'left' else 51]
371
+ self.held[side] = 0.08 < g < 0.85
372
+ self.note('closed', round(float(g), 3))
373
+ self.grasp_p = p
374
+ self.grip_pose[side] = (psi, al)
375
+ q, err = self.solve(side, p + [0, 0, 0.12], x, y, tool=tg, q0=q)
376
+ yield from self.arm_to(side, q, 40)
377
+
378
+ def held_point(self, side):
379
+ """hand-frame position of the centre of a held block."""
380
+ return tuple(HELD_OFF)
381
+
382
+ def place(self, side, Pw, release=True):
383
+ """Put the held block with its centre at world point Pw (lowering until contact)."""
384
+ self.phase = 'place_' + side
385
+ psi, al = self.grip_pose[side]
386
+ p = self.w2p(Pw)
387
+ tg = self.held_point(side)
388
+ best = None
389
+ az = self.azimuth(side, p)
390
+ cands = [psi + k * np.pi / 2 for k in range(-4, 5)]
391
+ cands = [ps for ps in cands if abs(wrap(ps - az)) < 0.8]
392
+ for ps in cands:
393
+ for a_ in (1.0, 1.2, 0.8, 1.4):
394
+ a, y, n, x = self.frame(ps, a_)
395
+ q1, e1 = self.solve(side, p + [0, 0, 0.07], x, y, tool=tg)
396
+ q2, e2 = self.solve(side, p, x, y, tool=tg, q0=q1)
397
+ c = e1 + 3 * e2 + 0.004 * abs(wrap(ps - az)) + 0.004 * abs(a_ - 1.0)
398
+ if best is None or c < best[0]:
399
+ best = (c, ps, a_, q1, e2)
400
+ _, psi, al, q1, e2 = best
401
+ a, y, n, x = self.frame(psi, al)
402
+ self.note('place-pose', np.round(p, 3), round(float(psi), 3), al, round(float(e2), 4))
403
+ yield from self.arm_to(side, q1, 45)
404
+ yield from self.wait(10)
405
+ q = q1
406
+ # descend in stages, closing the loop on the measured (FK) block position
407
+ corr = np.zeros(3)
408
+ contact = False
409
+ # xy is corrected while the block is still clear of the support (no friction),
410
+ # then the block goes straight down with z-only corrections
411
+ stages = [(0.07, 15, False), (0.07, 15, False), (0.035, 15, False)] + \
412
+ [(0.035, 8, False)] * 8 + [(PLACE_DZ, 18, True), (PLACE_DZ, 12, True)]
413
+ for z, n_wait, low in stages:
414
+ p = self.w2p(Pw) # the base drifts while standing: track the world target
415
+ m = self.hand_pose(side, tg)[:3, 3]
416
+ err = (p + [0, 0, z]) - m
417
+ if not low:
418
+ if z < 0.05 and np.hypot(err[0], err[1]) < 0.005:
419
+ continue
420
+ corr[:2] = np.clip(corr[:2] + np.clip(err[:2], -0.03, 0.03), -0.07, 0.07)
421
+ else:
422
+ corr[2] = np.clip(corr[2] + np.clip(err[2], -0.02, 0.02), -0.04, 0.04)
423
+ q, e = self.solve(side, p + [0, 0, z] + corr, x, y, tool=tg, q0=q, multi=False)
424
+ if low:
425
+ q0_ = self.q[side]
426
+ for i in range(1, 7):
427
+ self.set_arm(side, q0_ + (q - q0_) * i / 6)
428
+ yield from self.wait(2)
429
+ self.set_arm(side, q)
430
+ yield from self.wait(n_wait)
431
+ p = self.w2p(Pw)
432
+ m = self.hand_pose(side, tg)[:3, 3]
433
+ self.note('pre-release', np.round(m - p, 3), np.round(corr, 3))
434
+ z = PLACE_DZ
435
+ Ph = self.p2w(m)
436
+ self.note('placed', contact, round(z, 3), np.round(Ph, 3))
437
+ if release:
438
+ yield from self.grip(side, 0.0, 25)
439
+ self.held[side] = False
440
+ q, e = self.solve(side, p + [0, 0, z] - 0.06 * a, x, y, tool=tg, q0=q)
441
+ yield from self.arm_to(side, q, 20)
442
+ q, e = self.solve(side, p + [0, 0, z + 0.06] - 0.08 * a, x, y, tool=tg, q0=q)
443
+ yield from self.arm_to(side, q, 20)
444
+ return Ph
445
+
446
+ def carry_pose(self, side):
447
+ p = np.array([0.30, 0.20 if side == 'left' else -0.20, 0.10])
448
+ a, y, n, x = self.frame(0.0, 0.5)
449
+ q, err = self.solve(side, p, x, y)
450
+ return q
451
+
452
+ # ------------------------------------------------------------------ script
453
+ def head_blocks0(self):
454
+ """blocks on table 1 from the head camera at the initial state."""
455
+ im = self.tools.image('head')
456
+ Tc = cam.head_T(cam.PEL0, cam.WAIST0, 0.0, cam.CAM0)
457
+ found = []
458
+ for c in components(red_mask(im), 12):
459
+ found.append([cam.ray_plane(Tc, (c['u'], c['v']), BLOCK_Z, cam.CAM0[6]), c['n']])
460
+ # a block split by a gripper finger in front of it shows up as two blobs: merge
461
+ out = []
462
+ for P, n in sorted(found, key=lambda f: -f[1]):
463
+ for o in out:
464
+ if np.hypot(*(o[0][:2] - P[:2])) < 0.045:
465
+ o[0] = (o[0] * o[1] + P * n) / (o[1] + n)
466
+ o[1] += n
467
+ break
468
+ else:
469
+ out.append([P, n])
470
+ return [o[0] for o in out]
471
+
472
+ def approach_block(self, side, Pw):
473
+ """stand so that block Pw is in comfortable reach of `side`."""
474
+ self.phase = 'approach_' + side
475
+ sg = 1.0 if side == 'left' else -1.0
476
+ x = float(np.clip(Pw[0] - 0.36, 0.0, STAND_X_MAX))
477
+ y = float(Pw[1] - sg * 0.17)
478
+ yield from self.walk_to(x, y, 0.0, tol=0.02, ytol=0.04, speed=0.2)
479
+
480
+ def park_pose(self, side):
481
+ sg = 1.0 if side == 'left' else -1.0
482
+ p = np.array([0.16, sg * 0.36, 0.10])
483
+ a, y, n, x = self.frame(sg * 0.6, 0.6)
484
+ q, err = self.solve(side, p, x, y)
485
+ return q
486
+
487
+ def view_locate(self, side, Pw, zw, color='red', dist=VIEW_DIST):
488
+ """look at world point Pw with a wrist camera; return world position of the
489
+ nearest blob on plane zw (or None)."""
490
+ p = self.w2p(Pw)
491
+ p[2] = zw - self.o[23]
492
+ psi = self.azimuth(side, p)
493
+ a, y, n, x = self.frame(psi, VIEW_ALPHA)
494
+ tv = tuple(cam.wrist_point(side, dist))
495
+ q, err = self.solve(side, p + [0, 0, 0.06], x, y, tool=tv)
496
+ yield from self.arm_to(side, q, 30)
497
+ q, err = self.solve(side, p, x, y, tool=tv, q0=q)
498
+ yield from self.arm_to(side, q, 20)
499
+ yield from self.wait(12)
500
+ seen = self.wrist_locate(side, near=p, zw=zw, color=color)
501
+ if seen and np.hypot(*(seen[0]['P'][:2] - p[:2])) < 0.10:
502
+ P = self.p2w(seen[0]['P'])
503
+ P[2] = zw
504
+ self.note('view_locate', side, color, np.round(P, 3))
505
+ return P
506
+ self.note('view_locate-miss', side, color, len(seen))
507
+ return None
508
+
509
+ def wrist_project(self, side, mask, zw):
510
+ """world points where the pixels of `mask` hit the plane z = zw."""
511
+ o = self.o
512
+ Th = fk.arm_T(side, o[3:10] if side == 'left' else o[12:19], o[0:3])
513
+ c = cam.WCAM[side]
514
+ Tc = Th @ cam.wrist_offset(side)
515
+ ys, xs = np.nonzero(mask)
516
+ D = np.stack([np.full(len(xs), c[6]), 42.0 - xs, 42.0 - ys], 1) @ Tc[:3, :3].T
517
+ D = D[D[:, 2] < -1e-3]
518
+ t = ((zw - o[23]) - Tc[2, 3]) / D[:, 2]
519
+ pts = Tc[:3, 3][None, :] + D * t[:, None]
520
+ cy, sy = np.cos(o[24]), np.sin(o[24])
521
+ wx = o[21] + cy * pts[:, 0] - sy * pts[:, 1]
522
+ wy = o[22] + sy * pts[:, 0] + cy * pts[:, 1]
523
+ return np.stack([wx, wy], 1)
524
+
525
+ def wrist_pad_offset(self, side, b1):
526
+ """look down at block b1 on the pad; return pad centre minus block centre (xy)."""
527
+ p = self.w2p(b1)
528
+ psi = self.azimuth(side, p)
529
+ a, y, n, x = self.frame(psi, 1.1)
530
+ q, err = self.solve(side, p, x, y, tool=tuple(cam.wrist_point(side, 0.24)))
531
+ yield from self.arm_to(side, q, 30)
532
+ yield from self.wait(15)
533
+ im = self.tools.image(side + '_wrist')
534
+ g = green_mask(im)
535
+ lab, k = ndi.label(red_mask(im))
536
+ if g.sum() < 40 or k == 0:
537
+ return None
538
+ sizes = ndi.sum(np.ones_like(lab), lab, range(1, k + 1))
539
+ rb = self.wrist_project(side, lab == (1 + int(np.argmax(sizes))), BLOCK_Z).mean(0)
540
+ gp = self.wrist_project(side, g, TABLE_Z - 0.008)
541
+ lo, hi = gp.min(0), gp.max(0)
542
+ c = (lo + hi) / 2
543
+ for i in range(2):
544
+ if hi[i] - lo[i] < PAD_SIZE - 0.01:
545
+ # one side hidden under the block: trust the far edge
546
+ c[i] = hi[i] - PAD_SIZE / 2 if abs(rb[i] - lo[i]) < abs(rb[i] - hi[i]) else lo[i] + PAD_SIZE / 2
547
+ off = c - rb
548
+ nrm = np.hypot(*off)
549
+ if nrm > 0.05:
550
+ off = off * 0.05 / nrm
551
+ self.note('pad-view', np.round(c, 3), np.round(rb, 3), np.round(hi - lo, 3))
552
+ return off
553
+
554
+ def head_block_near(self, Pw, maxd=0.08):
555
+ """head-camera estimate (CAM0 model) of the block nearest world point Pw."""
556
+ im = self.tools.image('head')
557
+ o = self.o
558
+ Tc = cam.head_T(o[21:25], o[0:3], 0.0, cam.CAM0)
559
+ best = None
560
+ for c in components(red_mask(im), 12):
561
+ P = cam.ray_plane(Tc, (c['u'], c['v']), BLOCK_Z, cam.CAM0[6])
562
+ d = np.hypot(*(P[:2] - Pw[:2]))
563
+ if d < maxd and (best is None or d < best[0]):
564
+ best = (d, P)
565
+ return None if best is None else best[1]
566
+
567
+ def head_pad(self):
568
+ im = self.tools.image('head')
569
+ o = self.o
570
+ Tc = cam.head_T(o[21:25], o[0:3], 0.0, cam.CAM0)
571
+ cs = components(green_mask(im), 10)
572
+ if not cs:
573
+ return None
574
+ c = max(cs, key=lambda c: c['n'])
575
+ return cam.ray_plane(Tc, (c['u'], c['v']), TABLE_Z, cam.CAM0[6])
576
+
577
+ def run_place(self, side, Pw):
578
+ g = self.place(side, Pw)
579
+ while True:
580
+ try:
581
+ yield next(g)
582
+ except StopIteration as e:
583
+ self.last_placed = e.value
584
+ return
585
+
586
+ def run_view(self, side, Pw, zw, color='red'):
587
+ g = self.view_locate(side, Pw, zw, color)
588
+ while True:
589
+ try:
590
+ yield next(g)
591
+ except StopIteration as e:
592
+ self.last_seen = e.value
593
+ return
594
+
595
+ def get_block(self, side, Pw, tries=3):
596
+ """approach and grasp the block near Pw, retrying on a miss."""
597
+ for k in range(tries):
598
+ yield from self.approach_block(side, Pw)
599
+ yield from self.grasp(side, Pw)
600
+ if self.held[side]:
601
+ break
602
+ # missed: open, back off and look again
603
+ yield from self.grip(side, 0.0, 10)
604
+ yield from self.arm_to(side, self.carry_pose(side), 30)
605
+ self.phase = 'regrasp'
606
+ yield from self.run_view(side, Pw, BLOCK_Z)
607
+ if self.last_seen is not None:
608
+ Pw = self.last_seen
609
+ yield from self.arm_to(side, self.carry_pose(side), 30)
610
+
611
+ def script(self):
612
+ self.phase = 'look0'
613
+ blocks = self.head_blocks0()
614
+ blocks.sort(key=lambda P: -P[1])
615
+ self.note('blocks', [np.round(P, 3) for P in blocks])
616
+ while len(blocks) < 3:
617
+ blocks.append(np.array([0.55, 0.0, BLOCK_Z]))
618
+ self.blocks0 = blocks
619
+ # raise both hands out of the way, gripper open
620
+ for side in ('left', 'right'):
621
+ yield from self.arm_to(side, self.carry_pose(side), 30, grip=0.0)
622
+ right_b, left_b = blocks[-1], blocks[0]
623
+ third = blocks[1]
624
+ yield from self.get_block('right', right_b)
625
+ yield from self.get_block('left', left_b)
626
+
627
+ self.phase = 'trip1'
628
+ yield from self.go_table2()
629
+ pad = self.head_pad()
630
+ self.note('pad0', None if pad is None else np.round(pad, 3))
631
+ if pad is None:
632
+ pad = PAD_DEFAULT.copy()
633
+ yield from self.walk_to(pad[0] + T2_DX, pad[1], np.pi, tol=0.02, ytol=0.04, speed=0.15)
634
+ p2 = self.head_pad()
635
+ if p2 is not None and np.hypot(*(p2[:2] - pad[:2])) < 0.1:
636
+ pad = p2
637
+ self.note('pad', np.round(pad, 3))
638
+ yield from self.stack_two(pad)
639
+
640
+ self.phase = 'return'
641
+ side = 'left' if third[1] > 0.0 else 'right'
642
+ sg = 1.0 if side == 'left' else -1.0
643
+ yield from self.go_table1(float(np.clip(third[0] - 0.36, 0.0, STAND_X_MAX)), float(third[1] - sg * 0.17))
644
+ yield from self.wait(20)
645
+ seen = self.head_block_near(third, maxd=0.25)
646
+ if seen is not None:
647
+ third = seen
648
+ self.note('third', np.round(third, 3))
649
+ yield from self.get_block(side, third)
650
+
651
+ self.phase = 'trip2'
652
+ yield from self.go_table2()
653
+ top = self.stack_top
654
+ yield from self.walk_to(top[0] + T2_DX, top[1], np.pi, tol=0.02, ytol=0.04, speed=0.15)
655
+ other = 'right' if side == 'left' else 'left'
656
+ yield from self.arm_to(other, self.park_pose(other), 30)
657
+ yield from self.align_for(side, top)
658
+ yield from self.run_place(side, top + [0, 0, BLOCK])
659
+ yield from self.arm_to(side, self.park_pose(side), 40)
660
+ self.phase = 'done'
661
+ yield None
662
+
663
+ def align_for(self, side, Pw):
664
+ """stand facing -x with world point Pw in comfortable reach of `side`."""
665
+ sg = 1.0 if side == 'left' else -1.0
666
+ yield from self.walk_to(Pw[0] + T2_DX, Pw[1] + sg * 0.05, np.pi, tol=0.02, ytol=0.05, speed=0.15, n=400)
667
+
668
+ def stack_two(self, pad):
669
+ """left block on the pad, right block on top of it."""
670
+ yield from self.arm_to('right', self.park_pose('right'), 30)
671
+ target = np.array([pad[0], pad[1], BLOCK_Z])
672
+ yield from self.align_for('left', target)
673
+ yield from self.run_place('left', target)
674
+ b1 = np.array([self.last_placed[0], self.last_placed[1], BLOCK_Z])
675
+ yield from self.arm_to('left', self.park_pose('left'), 30)
676
+ # check block 1 against the pad with the free wrist camera (same image, bias cancels)
677
+ for attempt in range(PAD_CHECKS):
678
+ g = self.wrist_pad_offset('left', b1)
679
+ while True:
680
+ try:
681
+ yield next(g)
682
+ except StopIteration as e:
683
+ off = e.value
684
+ break
685
+ yield from self.arm_to('left', self.park_pose('left'), 30)
686
+ if off is None:
687
+ break
688
+ self.note('pad-check', np.round(off, 3))
689
+ if np.hypot(*off) < PAD_TOL:
690
+ break
691
+ yield from self.grasp('left', b1)
692
+ if not self.held['left']:
693
+ yield from self.grip('left', 0.0, 10)
694
+ yield from self.arm_to('left', self.park_pose('left'), 30)
695
+ break
696
+ yield from self.arm_to('left', self.carry_pose('left'), 25)
697
+ new = b1 + [off[0], off[1], 0.0]
698
+ yield from self.align_for('left', new)
699
+ yield from self.run_place('left', new)
700
+ b1 = np.array([self.last_placed[0], self.last_placed[1], BLOCK_Z])
701
+ yield from self.arm_to('left', self.park_pose('left'), 30)
702
+ yield from self.align_for('right', b1)
703
+ yield from self.run_place('right', b1 + [0, 0, BLOCK])
704
+ top = self.last_placed
705
+ self.stack_top = np.array([top[0], top[1], BLOCK_Z + BLOCK])
706
+ yield from self.arm_to('right', self.park_pose('right'), 30)
707
+
708
+ def go_table2(self):
709
+ yield from self.walk_to(0.0, self.o[22], None, final=False, speed=0.25) # back off table 1
710
+ for w in VIA:
711
+ yield from self.walk_to(w[0], w[1], None, final=False)
712
+ yield from self.walk_to(T2_STAND[0], T2_STAND[1], T2_STAND[2], tol=0.03, ytol=0.05, speed=0.2)
713
+
714
+ def go_table1(self, x, y):
715
+ yield from self.walk_to(T2_STAND[0] + 0.15, self.o[22], None, final=False, speed=0.25)
716
+ for w in VIA[::-1]:
717
+ yield from self.walk_to(w[0], w[1], None, final=False)
718
+ yield from self.walk_to(x, y, 0.0, tol=0.02, ytol=0.04, speed=0.2)
policies/wall_cupboard_close/astra-s1/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ seed,success,length,termination,fell
2
+ 620000,1,465,success,0
3
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+ 620099,1,465,success,0
policies/wall_cupboard_close/astra-s1/policy.py ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Hand-written cabinet closing motion with closed-loop base positioning.
2
+
3
+ The arms first clear the open leaves, then sweep inward together while
4
+ forward pressure seats the doors. All motion is indexed by simulation steps.
5
+ """
6
+
7
+ import numpy as np
8
+
9
+
10
+ class Policy:
11
+ def reset(self, obs, tools):
12
+ self.hold = np.asarray(tools.hold_action(), dtype=np.float32)
13
+
14
+ def act(self, obs, tools):
15
+ t = obs["t"]
16
+ action = self.hold.copy()
17
+
18
+ # Raise the hands outside the doors, approach, and sweep inward.
19
+ roll = 0.9 if t < 280 else max(0.2, 0.9 - (t - 280) * 0.003)
20
+ action[5:12] = [-1.3, roll, 0, 0.8, 0, 0, 0]
21
+ action[12:19] = [-1.3, -roll, 0, 0.8, 0, 0, 0]
22
+ action[19:] = 0
23
+
24
+ # Pose feedback compensates for walking lag and contact-induced drift.
25
+ x, y, _, yaw = obs["low_dim_obs"][21:25]
26
+ target_x = -0.27 if t < 80 else 0.45
27
+ error = target_x - x
28
+ action[0] = (
29
+ 0 if abs(error) < 0.015
30
+ else np.sign(error) * min(0.25, max(0.065, abs(error) * 1.5))
31
+ )
32
+ action[1] = (
33
+ 0 if abs(y) < 0.015
34
+ else -np.sign(y) * min(0.15, max(0.065, abs(y) * 1.5))
35
+ )
36
+ action[3] = np.clip(-yaw * 2, -0.2, 0.2)
37
+ return action
policies/wall_cupboard_close/astra-s2/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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policies/wall_cupboard_close/astra-s2/policy.py ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Deterministic sequential cabinet-door pushing controller."""
2
+ import numpy as np
3
+
4
+
5
+ class Policy:
6
+ def reset(self, obs, tools):
7
+ self.hold = np.asarray(tools.hold_action(), dtype=np.float32)
8
+
9
+ def act(self, obs, tools):
10
+ # Approach the outside of each door, sweep towards the middle, then
11
+ # follow through towards the wall. Keep the unused hand below the doors.
12
+ sequence = [
13
+ (-.10, .50, 0), (.05, .50, 1), (.05, .15, 1),
14
+ (.25, .00, 1), (.25, .00, 0), (-.10, -.50, 0),
15
+ (.05, -.50, 2), (.05, -.15, 2), (.25, .00, 2),
16
+ (.25, .00, 0),
17
+ ]
18
+ stage = min(int(obs['t']) // 160, len(sequence)-1)
19
+ x, y, side = sequence[stage]
20
+ a = self.hold.copy()
21
+ a[5:19] = 0
22
+ a[19:] = 0
23
+ if side:
24
+ k = 5 if side == 1 else 12
25
+ s = 1 if side == 1 else -1
26
+ a[k:k+7] = [-2.2, s*.2, 0, .6, 0, .9, 0]
27
+ pos = obs['low_dim_obs'][21:25]
28
+ dx, dy = x-pos[0], y-pos[1]
29
+ a[0] = np.clip(dx*2, -.22, .22) if abs(dx) > .02 else 0
30
+ a[1] = np.clip(dy*2, -.22, .22) if abs(dy) > .02 else 0
31
+ a[3] = -pos[3]*2
32
+ return a
policies/wall_cupboard_close/astra-s3/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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policies/wall_cupboard_close/astra-s3/policy.py ADDED
@@ -0,0 +1,34 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Deterministic cabinet closing with symmetric, gradual arm sweeps."""
2
+ import numpy as np
3
+
4
+ class Policy:
5
+ def reset(self, obs, tools):
6
+ self.action = np.asarray(tools.hold_action(), dtype=np.float32)
7
+ self.action[19:] = 0
8
+ self.stages = [(130,-.27,1.1),(180,.05,1.1),(180,.05,-.2),
9
+ (130,.05,1.1),(130,.22,1.1),(180,.22,-.2),
10
+ (130,.22,1.1),(130,.4,1.1),(180,.4,-.2)]
11
+ self.stage=0
12
+ self.start=0
13
+ self.initial=self.action.copy()
14
+
15
+ def act(self, obs, tools):
16
+ t=obs['t']
17
+ if self.stage<len(self.stages)-1 and t-self.start>=self.stages[self.stage][0]:
18
+ self.stage+=1
19
+ self.start=t
20
+ self.initial=self.action.copy()
21
+ _,x,roll=self.stages[self.stage]
22
+ q=obs['low_dim_obs']
23
+ a=self.action.copy()
24
+ dx=x-q[21]; dy=-q[22]
25
+ a[0]=np.clip(1.5*dx,-.18,.18) if abs(dx)>.015 else 0
26
+ a[1]=np.clip(1.5*dy,-.12,.12) if abs(dy)>.015 else 0
27
+ speed=np.linalg.norm(a[:2])
28
+ if 0<speed<.06:a[:2]*=.06/speed
29
+ a[3]=np.clip(-2*q[24],-.3,.3)
30
+ target=np.array([-2.7,roll,0,.3,0,1.4,0,-2.7,-roll,0,.3,0,1.4,0])
31
+ u=min(1,(t-self.start+1)/100)
32
+ a[5:19]=self.initial[5:19]*(1-u)+target*u
33
+ self.action=a
34
+ return a
policies/wall_cupboard_close/deepseek1017-s1/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ seed,success,length,termination,fell
2
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3
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33
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37
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38
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39
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51
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52
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53
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56
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57
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58
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59
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60
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61
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62
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63
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64
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65
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66
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67
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68
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69
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70
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71
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72
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73
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74
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75
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76
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77
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78
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79
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80
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81
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82
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83
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85
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86
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87
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88
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89
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90
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91
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92
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93
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94
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95
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96
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97
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98
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99
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100
+ 620098,0,1700,timeout,0
101
+ 620099,0,1700,timeout,0
policies/wall_cupboard_close/deepseek1017-s1/policy.py ADDED
@@ -0,0 +1,149 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Control policy for the Unitree G1 "close the wall-cabinet doors" task.
2
+
3
+ The reset scene is deterministic: the robot starts just in front of the
4
+ kitchen counter and the wall cabinet (with its two doors) is mounted above
5
+ it. The policy walks up to the counter, raises both arms to the height of
6
+ the cabinet bottom and then executes a sequence of pushes (upward, forward,
7
+ outward, inward and a downward sweep). The hands are then withdrawn so the
8
+ doors are not held by them, and the whole approach/push sequence is repeated
9
+ once more at a slightly different arm pose.
10
+
11
+ No learning, no randomness and no wall-clock dependency: the action is a
12
+ pure function of the control step (``obs["t"]``), so every episode is
13
+ reproducible from the reset.
14
+ """
15
+
16
+ import numpy as np
17
+
18
+
19
+ class Policy:
20
+ """Hand-written controller for the wall-cabinet-closing task."""
21
+
22
+ def reset(self, obs, tools):
23
+ """Remember the pose-holding action (zero base velocity)."""
24
+ self.hold = np.asarray(tools.hold_action(), dtype=np.float32)
25
+ self.t = 0
26
+
27
+ @staticmethod
28
+ def _ramp(t, t0, t1, a, b):
29
+ """Linear interpolation between ``a`` at ``t0`` and ``b`` at ``t1``."""
30
+ if t <= t0:
31
+ return float(a)
32
+ if t >= t1:
33
+ return float(b)
34
+ f = (t - t0) / float(t1 - t0)
35
+ return float(a + (b - a) * f)
36
+
37
+ def _arm_targets(self, t):
38
+ """Return (shoulder_pitch, roll, yaw, elbow) for the current step."""
39
+ if t < 120:
40
+ sp = self._ramp(t, 0, 120, 0.0, -1.60)
41
+ sr = self._ramp(t, 0, 120, 0.0, 0.45)
42
+ el = self._ramp(t, 0, 120, 0.0, 0.70)
43
+ return sp, sr, 0.0, el
44
+ if t < 210:
45
+ sp = self._ramp(t, 120, 210, -1.60, -2.60)
46
+ el = self._ramp(t, 120, 210, 0.70, 0.40)
47
+ return sp, 0.45, 0.0, el
48
+ if t < 300:
49
+ return -2.00, 0.25, 0.0, 1.00
50
+ if t < 390:
51
+ return -2.00, self._ramp(t, 300, 390, 0.25, 1.55), 0.0, 1.00
52
+ if t < 480:
53
+ return -2.00, self._ramp(t, 390, 480, 1.55, -0.20), 0.0, 1.00
54
+ if t < 570:
55
+ sp = self._ramp(t, 480, 570, -2.00, -0.80)
56
+ el = self._ramp(t, 480, 570, 1.00, 0.30)
57
+ return sp, 0.30, 0.0, el
58
+ if t < 660:
59
+ sp = self._ramp(t, 570, 660, -1.70, -2.75)
60
+ el = self._ramp(t, 570, 660, 0.60, 0.10)
61
+ return sp, 0.20, 0.0, el
62
+ if t < 780:
63
+ return -1.90, 0.20, 0.0, 0.15
64
+ if t < 900:
65
+ sp = self._ramp(t, 780, 900, -0.60, -2.60)
66
+ el = self._ramp(t, 780, 900, 1.80, 0.20)
67
+ sr = self._ramp(t, 780, 900, 0.00, 0.30)
68
+ return sp, sr, 0.0, el
69
+ if t < 1000:
70
+ el = self._ramp(t, 900, 1000, 0.20, 0.90)
71
+ sr = self._ramp(t, 900, 1000, 0.30, 1.40)
72
+ return -2.40, sr, 0.0, el
73
+ if t < 1120:
74
+ sp = self._ramp(t, 1000, 1120, -2.40, 0.0)
75
+ el = self._ramp(t, 1000, 1120, 0.90, 0.0)
76
+ sr = self._ramp(t, 1000, 1120, 1.40, 0.0)
77
+ return sp, sr, 0.0, el
78
+ if t < 1300:
79
+ # second attempt: walk in again with the hands in front
80
+ sp = self._ramp(t, 1200, 1300, 0.0, -1.70)
81
+ sr = self._ramp(t, 1200, 1300, 0.0, 0.35)
82
+ el = self._ramp(t, 1200, 1300, 0.0, 0.90)
83
+ return sp, sr, 0.0, el
84
+ if t < 1420:
85
+ sp = self._ramp(t, 1300, 1420, -1.70, -2.70)
86
+ el = self._ramp(t, 1300, 1420, 0.90, 0.20)
87
+ return sp, 0.30, 0.0, el
88
+ if t < 1520:
89
+ return -1.90, self._ramp(t, 1420, 1520, 0.30, 1.40), 0.0, 0.80
90
+ if t < 1600:
91
+ return -1.90, self._ramp(t, 1520, 1600, 1.40, -0.10), 0.0, 0.80
92
+ if t < 1700:
93
+ sp = self._ramp(t, 1600, 1700, -1.90, -2.70)
94
+ el = self._ramp(t, 1600, 1700, 0.80, 0.10)
95
+ return sp, 0.30, 0.0, el
96
+ return 0.0, 0.0, 0.0, 0.0
97
+
98
+ def act(self, obs, tools):
99
+ """Return the 21-float physical action for this control step."""
100
+ t = int(obs.get("t", self.t))
101
+ self.t = t
102
+ a = self.hold.copy()
103
+
104
+ # ---- base ------------------------------------------------------
105
+ if t < 150:
106
+ vx = 0.30
107
+ elif t < 240:
108
+ vx = 0.15
109
+ elif t < 340:
110
+ vx = 0.35
111
+ elif t < 520:
112
+ vx = 0.12
113
+ elif t < 620:
114
+ vx = 0.30
115
+ elif t < 700:
116
+ vx = 0.15
117
+ elif t < 760:
118
+ vx = -0.25
119
+ elif t < 900:
120
+ vx = 0.30
121
+ elif t < 1000:
122
+ vx = 0.10
123
+ elif t < 1120:
124
+ vx = -0.30
125
+ elif t < 1300:
126
+ vx = 0.28
127
+ elif t < 1420:
128
+ vx = 0.15
129
+ elif t < 1520:
130
+ vx = 0.25
131
+ elif t < 1600:
132
+ vx = 0.10
133
+ elif t < 1680:
134
+ vx = 0.25
135
+ else:
136
+ vx = -0.25
137
+ a[0] = vx
138
+ a[1] = 0.0
139
+ a[2] = 0.74
140
+ a[3] = 0.0
141
+ a[4] = 0.0
142
+
143
+ # ---- arms ------------------------------------------------------
144
+ sp, sr, sy, el = self._arm_targets(t)
145
+ a[5:12] = np.array([sp, sr, sy, el, 0.0, 0.0, 0.0], np.float32)
146
+ a[12:19] = np.array([sp, -sr, -sy, el, 0.0, 0.0, 0.0], np.float32)
147
+ a[19] = 0.85
148
+ a[20] = 0.85
149
+ return a
policies/wall_cupboard_close/deepseek1017-s2/episodes.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
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policies/wall_cupboard_close/deepseek1017-s2/policy.py ADDED
@@ -0,0 +1,122 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Close both open doors of the wall cabinet.
2
+
3
+ The policy is a hand-written, open-loop upper-body trajectory with a small
4
+ base-position feedback term. It was tuned by running episodes on the
5
+ development seeds; there is no learned component and no data lookup table.
6
+
7
+ Behaviour
8
+ 1. Step 0-249: bring both hands up in front of the wall cupboard and creep
9
+ towards the counter while keeping the pelvis near x = 0.10 m.
10
+ 2. Step 250 onwards: apply a slow multi-frequency sweep of the shoulder
11
+ pitch / yaw and elbow, plus small wrist-roll and gripper oscillations,
12
+ while nudging the base into the doors. The different periods make the
13
+ two hands sweep the door faces in a quasi-periodic pattern, which pushes
14
+ both doors shut. Occasional firmer shoves (t % 400 < 30) help seat the
15
+ doors in their latches.
16
+
17
+ The motion only uses proprioception (pelvis x from low_dim_obs) for feedback,
18
+ so it is deterministic and needs no image processing at run time.
19
+ """
20
+
21
+ import numpy as np
22
+
23
+
24
+ class Policy:
25
+ """Deterministic scripted controller for the wall-cabinet task."""
26
+
27
+ def reset(self, obs, tools):
28
+ """Capture the initial hold action and reset the step counter.
29
+
30
+ Args:
31
+ obs: The first observation.
32
+ tools: The tools described in docs/api.md.
33
+ """
34
+ # Keep the reset-pose targets (arms down, height/pitch/velocity zero,
35
+ # half-closed grippers) as the base of every command.
36
+ self.hold = np.asarray(tools.hold_action(), dtype=np.float32).copy()
37
+ self.step = 0
38
+ self.two_pi = 2.0 * np.pi
39
+
40
+ def act(self, obs, tools):
41
+ """Return the physical action for this control step.
42
+
43
+ Args:
44
+ obs: The current observation.
45
+ tools: The tools described in docs/api.md.
46
+
47
+ Returns:
48
+ The 21-float physical action.
49
+ """
50
+ i = self.step
51
+ self.step += 1
52
+ x = float(obs["low_dim_obs"][21])
53
+ a = self.hold.copy()
54
+
55
+ if i < 250:
56
+ # Reach: hands up at the cupboard height, walking slowly in.
57
+ self._arm(a, pitch=-1.2, roll=0.2, elbow=0.4, yaw=0.0,
58
+ wrist_roll=0.0, wrist_pitch=0.0, grip=0.5)
59
+ err = 0.10 - x
60
+ a[0] = float(np.clip(1.5 * err, -0.15, 0.15)) if abs(err) > 0.02 else 0.0
61
+ return a
62
+
63
+ t = i - 250
64
+ w = self.two_pi
65
+ # Quasi-periodic sweep of the arms over the door faces.
66
+ pitch = -1.3 + 0.45 * np.sin(w * t / 150.0) + 0.1 * np.sin(w * t / 53.0)
67
+ yaw = 0.55 * np.sin(w * t / 230.0)
68
+ roll = 0.2
69
+ elbow = 0.45 + 0.25 * np.sin(w * t / 110.0)
70
+ wrist_roll = 0.7 * np.sin(w * t / 190.0)
71
+ grip = 0.5 + 0.5 * np.sin(w * t / 300.0)
72
+
73
+ self._arm(a, pitch=pitch, roll=roll, elbow=elbow, yaw=yaw,
74
+ wrist_roll=wrist_roll, wrist_pitch=0.0, grip=grip)
75
+
76
+ # Lean into the cupboard and keep the pelvis at the counter.
77
+ err = 0.10 - x
78
+ a[0] = float(np.clip(1.2 * err, -0.08, 0.08)) if abs(err) > 0.02 else 0.05
79
+ a[4] = 0.15 * np.sin(w * t / 170.0)
80
+
81
+ # Periodic firmer shove: drive the torso forward and step in.
82
+ if t % 400 < 30:
83
+ a[0] = 0.25
84
+ a[4] = 0.6
85
+
86
+ return a
87
+
88
+ @staticmethod
89
+ def _arm(a, pitch, roll, elbow, yaw, wrist_roll, wrist_pitch, grip):
90
+ """Write symmetric arm and gripper targets into ``a`` in place.
91
+
92
+ Args:
93
+ a: 21-float action buffer.
94
+ pitch: Shoulder pitch for both arms, rad.
95
+ roll: Left shoulder roll, rad (right arm mirrors it).
96
+ elbow: Elbow flexion for both arms, rad.
97
+ yaw: Left shoulder yaw, rad (right arm mirrors it).
98
+ wrist_roll: Left wrist roll, rad (right arm mirrors it).
99
+ wrist_pitch: Wrist pitch for both arms, rad.
100
+ grip: Gripper command for both hands, 0 open .. 1 closed.
101
+
102
+ Returns:
103
+ The same action buffer ``a``.
104
+ """
105
+ a[5] = pitch
106
+ a[6] = roll
107
+ a[7] = yaw
108
+ a[8] = elbow
109
+ a[9] = wrist_roll
110
+ a[10] = wrist_pitch
111
+ a[11] = 0.0
112
+ a[19] = grip
113
+
114
+ a[12] = pitch
115
+ a[13] = -roll
116
+ a[14] = -yaw
117
+ a[15] = elbow
118
+ a[16] = -wrist_roll
119
+ a[17] = wrist_pitch
120
+ a[18] = 0.0
121
+ a[20] = grip
122
+ return a