| --- |
| license: mit |
| tags: |
| - robotics |
| - imitation-learning |
| - behavioral-cloning |
| - libero |
| - boss-benchmark |
| --- |
| |
| # BOSS — pre-trained skill policies |
|
|
| Skill policies for the three Behavioral Cloning baselines of |
| [**BOSS: Benchmark for Observation Space Shift in Long-Horizon Task**](https://arxiv.org/abs/2502.15679) |
| (Yang et al., *IEEE Robotics and Automation Letters*, 2025), |
| so the benchmark can be run without re-training. |
|
|
| Code: https://github.com/YY-GX/BOSS · Project page: https://boss-benchmark.github.io/ |
|
|
| ## Contents |
|
|
| | Folder | Baseline | Files | |
| |---|---|---| |
| | `BCRNNPolicy_seed10000/run_001/` | BC-RESNET-RNN | `task0..task43_model.pth` | |
| | `BCTransformerPolicy_seed10000/run_001/` | BC-RESNET-T | `task0..task43_model.pth` | |
| | `BCViLTPolicy_seed10000/run_001/` | BC-VIT-T | `task0..task43_model.pth` | |
|
|
| One checkpoint per skill, indexed by position in the `boss_44` task list. All |
| trained with `seed=10000`, observation space = third-person camera + 7-DoF joint |
| angles + 2-DoF gripper state (no wrist camera), matching Section V-A of the paper. |
|
|
| ## Usage |
|
|
| ```shell |
| huggingface-cli download yygx/BOSS-checkpoints --local-dir experiments/boss_44/0.0.0 |
| |
| python libero/lifelong/eval_skills_unaffected_by_oss.py \ |
| --benchmark boss_44 --seed 10000 --max_steps 400 \ |
| --model_path_folder experiments/boss_44/0.0.0/BCTransformerPolicy_seed10000/run_001/ |
| ``` |
|
|
| ## Important: `--max_steps 400` |
| |
| Evaluation hyper-parameters are read from the checkpoint, and these checkpoints |
| carry `max_steps=600` while the published results used **400**. On BOSS-C3 task 1, |
| BC-RESNET-T reaches a Delta to Upper Bound Ratio of 89% at 400 steps — the value |
| in Figure 5 — and 67% at 600. Always pass `--max_steps 400` to reproduce the paper. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{yang2025boss, |
| title={BOSS: Benchmark for observation space shift in long-horizon task}, |
| author={Yang, Yue and Zhao, Linfeng and Ding, Mingyu and Bertasius, Gedas and Szafir, Daniel}, |
| journal={IEEE Robotics and Automation Letters}, |
| volume={10}, |
| number={9}, |
| pages={8882--8889}, |
| year={2025}, |
| publisher={IEEE} |
| } |
| ``` |
|
|
| Built on [LIBERO](https://github.com/Lifelong-Robot-Learning/LIBERO) (Liu et al., 2023). |
|
|
