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--- |
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license: cc-by-4.0 |
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viewer: false |
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--- |
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# Model Checkpoints for ManiSkill-HAB |
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**[Paper](https://arxiv.org/abs/2412.13211)** |
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| **[Website](https://arth-shukla.github.io/mshab)** |
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| **[Code](https://github.com/arth-shukla/mshab)** |
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| **[Models](https://huggingface.co/arth-shukla/mshab_checkpoints)** |
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| **[Dataset](https://arth-shukla.github.io/mshab/#dataset-section)** |
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| **[Supplementary](https://sites.google.com/view/maniskill-hab)** |
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RL (SAC, PPO) and IL (BC, DP) baselines for ManiSkill-HAB. Each checkpoint includes a torch checkpoint `policy.pt` (model, optimizer/scheduler state, other trainable parameters) and a train config `config.yml` with hyperparemeters and env kwargs. |
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RL Pick/Place policies are trained using SAC due to improved performance, while Open/Close is trained with PPO for wall-time efficiency (see Appendix A.4.3). All-object RL policies are under `all/` directories, while per-object policies are under directories labeled by the object name. IL policies do not have per-object Pick/Place variants. |
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To download these policies, run the following: |
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``` |
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huggingface-cli download arth-shukla/mshab_checkpoints --local-dir mshab_checkpoints |
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``` |
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If you use ManiSkill-HAB in your work, please consider citing the following: |
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``` |
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@inproceedings{shukla2025maniskillhab, |
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author = {Arth Shukla and |
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Stone Tao and |
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Hao Su}, |
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title = {ManiSkill-HAB: {A} Benchmark for Low-Level Manipulation in Home Rearrangement |
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Tasks}, |
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booktitle = {The Thirteenth International Conference on Learning Representations, |
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{ICLR} 2025, Singapore, April 24-28, 2025}, |
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publisher = {OpenReview.net}, |
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year = {2025}, |
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url = {https://openreview.net/forum?id=6bKEWevgSd}, |
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timestamp = {Thu, 15 May 2025 17:19:05 +0200}, |
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biburl = {https://dblp.org/rec/conf/iclr/ShuklaTS25.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
