| --- |
| language: |
| - en |
| license: mit |
| task_categories: |
| - robotics |
| pretty_name: BRS Data |
| tags: |
| - robot-manipulation |
| - imitation-learning |
| - real-world-data |
| --- |
| |
| # Dataset Card for BEHAVIOR Robot Suite (BRS) Data |
|
|
| This dataset provides robotic trajectories for five real-world household tasks. These tasks are: |
|
|
| 1. Clean house after a wild party; |
| 2. Clean the toilet; |
| 3. Take trash outside; |
| 4. Put items onto shelves; |
| 5. Lay clothes out. |
|
|
| These data are first collected and used in the paper [BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities |
| ](https://huggingface.co/papers/2503.05652). |
|
|
| ## Dataset Details |
|
|
| ### Dataset Description |
|
|
| - **Curated by:** [Yunfan Jiang](https://yunfanj.com/) |
| - **License:** [MIT](LICENSE) |
|
|
| ### Dataset Sources |
|
|
| - **Repository:** https://github.com/behavior-robot-suite/brs-algo |
| - **Paper:** https://arxiv.org/abs/2503.05652 |
| - **Project page:** https://behavior-robot-suite.github.io/ |
|
|
| ## Uses |
|
|
| For usage instructions, see our doc [here](https://behavior-robot-suite.github.io/docs/sections/wbvima/overview.html). |
|
|
| ## Sample Usage |
|
|
| To train a WB-VIMA policy, simply run the following command as described in the [official documentation](https://behavior-robot-suite.github.io/docs/sections/wbvima/overview.html): |
|
|
| ```bash |
| python3 main/train/train.py data_dir=<HDF5_PATH> \ |
| bs=<BS> \ |
| arch=wbvima \ |
| task=<TASK_NAME> \ |
| exp_root_dir=<EXP_ROOT_DIR> \ |
| gpus=<NUM_GPUS> \ |
| use_wandb=<USE_WANDB> \ |
| wandb_project=<WANDB_PROJECT> |
| ``` |
|
|
| To deploy a WB-VIMA policy on the real robot, simply run the following command: |
|
|
| ```bash |
| python3 main/rollout/<TASK_NAME>/rollout_async.py --ckpt_path <CKPT_PATH> --action_execute_start_idx <IDX> |
| ``` |
|
|
| ## Citation |
|
|
| **BibTeX:** |
|
|
| ``` |
| @article{jiang2025brs, |
| title = {BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities}, |
| author = {Yunfan Jiang and Ruohan Zhang and Josiah Wong and Chen Wang and Yanjie Ze and Hang Yin and Cem Gokmen and Shuran Song and Jiajun Wu and Li Fei-Fei}, |
| year = {2025}, |
| journal = {arXiv preprint arXiv: 2503.05652} |
| } |
| ``` |
|
|
| ## Dataset Card Author and Contact |
|
|
| [Yunfan Jiang](https://yunfanj.com/) |