DreamMachines/actuator_unboxing_4h_lightbox

Actuator unboxing, 4 h from the lightbox only

A four-hour subset drawn only from the lightbox, the scene every evaluation in the post runs in. The single-scene control for the scene-diversity comparison.

One of the nine training datasets behind the Dream Machines post Empirical results fine-tuning π0.5 on a real manufacturing task (September 2026), which fine-tunes π0.5 on this task and reports what mattered: training duration, LoRA vs. full fine-tuning, data quantity, scene diversity, data quality, intervention data and serving settings. Used in Section 2.7.

The task

Actuators arrive in cardboard boxes and must be transferred into a tray that fixes their position and orientation for a downstream production line. Each actuator is inserted with its bottom contacts correctly oriented, with both possible 180° rotations acceptable. Only the top layer of the source box is used: 40 actuators. The left arm picks an actuator from the box and hands it to the right arm, which inserts it into one of the tray's 135 holes, arranged in 9 rows of 15. The order is fixed: actuators leave the box right to left and bottom to top, and fill the tray left to right and bottom to top. Every episode starts and ends in a rest position.

The post describes the task, the collection and the evaluation protocol in full: Section 1.

This dataset

• Lightbox episodes only, both operators.
• Same budget as actuator_unboxing_4h_diverse, which spreads the four hours over five scenes.

Collection

• Robot: bimanual TRLC-DK1 (bi_dk1_follower), recorded at 50 Hz.
• Cameras: three fisheye cameras (left wrist, right wrist, top context) recorded at 640×480 MJPG and centre-cropped to 224×224 in this export, matching π0.5's SigLIP input size.
• Teleoperation: two operators, one with a VR headset (our open-source VR teleop kit), one with Gello leader arms.
• Scenes: the lightbox only, day and night; box and tray positions randomised independently over about 11×11 cm.
• Filtering: every episode was rated 1 to 5 (1 an irrecoverable failure, 3 a minor mistake, 5 a clean execution); only episodes rated 3 or above are kept, and idle periods at the start and end are trimmed.

Contents

Episodes	1,569
Frames	713,067 (4.0 h at 50 Hz)
Cameras	3: left_wrist, right_wrist, top, 224×224
State / action	14-dim: six joint positions and one gripper per arm, absolute joint targets
Format	LeRobot v3.0

Results in the post

• Full fine-tune, 50k steps: 33% (13/40), against 63% for the five-scene set of the same size, despite being collected entirely in the evaluation scene.

All evaluations: 40 rollouts per round in the lightbox, one per actuator of the top layer, end-to-end success with Wilson 95% intervals; see the evaluation protocol.

Related datasets

• DreamMachines/actuator_unboxing_20h_345_v2: Actuator unboxing, 21 h (main training set)
• DreamMachines/actuator_unboxing_20h_345: Actuator unboxing, 21 h (earlier export)
• DreamMachines/actuator_unboxing_10h_vr: Actuator unboxing, operator 1 (VR teleoperation), 10 h
• DreamMachines/actuator_unboxing_10h_leaderarms: Actuator unboxing, operator 2 (Gello leader arms), 10 h
• DreamMachines/actuator_unboxing_10h_diverse: Actuator unboxing, 10 h across operators and scenes
• DreamMachines/actuator_unboxing_4h_diverse: Actuator unboxing, 4 h across operators and scenes
• DreamMachines/actuator_unboxing_1h_HQ: Actuator unboxing, one high-quality hour
• DreamMachines/actuator_unboxing_1h_HQ_dagger_intv: Actuator unboxing, one high-quality hour plus intervention data

Citation

@misc{dreammachines2026pi05,
  title  = {Empirical results fine-tuning π0.5 on a real manufacturing task},
  author = {Dream Machines},
  year   = {2026},
  url    = {https://dream-machines.eu/blog/pi05-fine-tuning}
}