Buckets:
| # PointWorld-BEHAVIOR WebDataset shards | |
| WebDataset (WDS) train/test shards for training and evaluating | |
| [PointWorld](https://github.com/NVlabs/PointWorld), converted from the | |
| [`nvidia/PointWorld-BEHAVIOR`](https://huggingface.co/datasets/nvidia/PointWorld-BEHAVIOR) | |
| release. This is not an official NVIDIA release. | |
| ## License | |
| **NVIDIA License, non-commercial: research and evaluation use only.** See `LICENSE.pdf`. | |
| This data is a derivative work of `nvidia/PointWorld-BEHAVIOR`, which is distributed under | |
| the NVIDIA License, and is redistributed under that same license. The NVIDIA License permits | |
| use of this data and of anything derived from it only for non-commercial purposes, meaning | |
| research or evaluation. The underlying simulation episodes come from | |
| [BEHAVIOR-1K](https://behavior.stanford.edu/) | |
| ([`behavior-1k/2025-challenge-rawdata`](https://huggingface.co/datasets/behavior-1k/2025-challenge-rawdata)); | |
| see its terms as well. | |
| ## Contents | |
| | Split | Clips (samples) | Shards | | |
| |---|---|---| | |
| | train | 1,557,489 | `behavior/wds/train/train-rank*-*.tar` | | |
| | test | 173,104 | `behavior/wds/test/test-rank*-*.tar` | | |
| ``` | |
| behavior/wds/ | |
| ├── train/train-rank{R}-NNNNNN.tar | |
| ├── test/test-rank{R}-NNNNNN.tar | |
| ├── metadata_rank{R}.json | |
| └── {train,test}_source_paths_rank{R}.txt | |
| _jobs/ | |
| ├── wds_index.json # samples per tar | |
| ├── integrity_check.json # merged integrity results (valid clips) | |
| ├── unavailable_test_clips.json # paper test clips not in the source release | |
| └── ... # build bookkeeping | |
| ``` | |
| The layout matches what the PointWorld `main` branch expects: point `LOCAL_DATASET_DIR` | |
| at a directory containing `behavior/wds`. You can mount the bucket there or sync it | |
| locally (about 1.8 TB). | |
| ## How it was made | |
| Built with the `data` branch pipeline (`data_integrity_check.py` + `convert_wds.py`), run | |
| per source task on Hugging Face Jobs. | |
| Very minor differences from the paper: | |
| - **Split:** the paper split `manifests/behavior_paper_split_manifest.json`, except for 483 | |
| manifest test clips from 19 episodes. These have legacy ids (e.g. | |
| `boxing_books_up_for_storage_1753068141443199_boxing_books_up_for_storage-2735:2746`) | |
| with no counterpart in the source release, so the test split has 173,104 of the | |
| manifest's 173,587 clips. The 483 ids are listed in `_jobs/unavailable_test_clips.json`. | |
| - **Clip totals:** the source release yields 1,730,593 valid clips against the paper's | |
| 1,735,872. The gap of 5,279 matches those 19 episodes (train + test) plus about 450 clips, | |
| so the episodes appear to be absent from the release rather than present in train. | |
| - **Content:** WDS samples contain the fields listed by `get_wds_data_keys("behavior")` in | |
| `shared/data_contract.py`, plus `clip_attributes`. Clips excluded by the BEHAVIOR | |
| integrity filtering in `data_integrity_check.py` are not included. | |
| ## Citation | |
| If you use this data, cite the PointWorld paper and the original BEHAVIOR dataset | |
| (https://behavior.stanford.edu/). | |
| ``` | |
| @article{huang2026pointworld, | |
| title={PointWorld: Scaling 3D World Models for In-The-Wild Robotic Manipulation}, | |
| author={Huang, Wenlong and Chao, Yu-Wei and Mousavian, Arsalan and Liu, Ming-Yu and Fox, Dieter and Mo, Kaichun and Li, Fei-Fei}, | |
| journal={arXiv preprint arXiv:2601.03782}, | |
| year={2026} | |
| } | |
| ``` | |
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