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Model Checkpoints for ManiSkill-HAB
Paper | Website | Code | Models | Dataset | Supplementary
RL (SAC, PPO) and IL (BC) 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.
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.
To download these policies, run the following:
huggingface-cli download arth-shukla/mshab_checkpoints --local-dir mshab_checkpoints
If you use ManiSkill-HAB in your work, please consider citing the following:
@article{shukla2024maniskillhab,
author = {Arth Shukla and Stone Tao and Hao Su},
title = {ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement Tasks},
journal = {CoRR},
volume = {abs/2412.13211},
year = {2024},
url = {https://doi.org/10.48550/arXiv.2412.13211},
doi = {10.48550/ARXIV.2412.13211},
eprinttype = {arXiv},
eprint = {2412.13211},
timestamp = {Mon, 09 Dec 2024 01:29:24 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2412-13211.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}