---
pretty_name: "Behavior-Skill"
language:
  - en
license: mit
task_categories:
  - robotics
tags:
  - vision-language-action
  - embodied-ai
  - mobile-manipulation
  - skill-learning
  - skill-evaluation
size_categories:
  - "100K<n<1M"
---

# Behavior-Skill

**Behavior-Skill** is a skill-centric dataset and evaluation benchmark built on [BEHAVIOR-1K](https://behavior.stanford.edu/) for Vision-Language-Action (VLA) policies in long-horizon mobile manipulation tasks. It establishes executable constituent skills as the fundamental unit for both policy learning and evaluation.

**Paper:** [arXiv](https://arxiv.org/abs/2608.30536) &nbsp;|&nbsp; **Code:** [GitHub](https://github.com/mafangniu/Behavior-Skill)

Behavior-Skill contains **235,492 skill instances** constructed from **10,000 demonstrations** across **50 household tasks** and **34 semantic skill categories**. Each annotated instance contains a skill instruction and its aligned frame interval in the corresponding BEHAVIOR-1K demonstration. For **500 evaluation demonstrations** (10 per task), the release additionally provides per-skill BDDL success conditions and restorable OmniGibson states for independent closed-loop evaluation.

The original observations and action trajectories are available from the upstream [BEHAVIOR-1K demonstrations](https://huggingface.co/datasets/behavior-1k/2025-challenge-demos) and are referenced by frame intervals; they are not duplicated in this repository.

## Dataset at a Glance

| Statistic | Value |
| --- | ---: |
| Source benchmark | BEHAVIOR-1K |
| Household tasks | 50 |
| Demonstrations | 10,000 |
| Skill instances | 235,492 |
| Semantic skill categories | 34 |
| Average skill duration | 16.6 s |
| Evaluation demonstrations | 500 (10 per task) |

## Repository Contents

| Path | Contents | Primary use |
| --- | --- | --- |
| `skill_annotations/` | Episode-level skill instructions and aligned frame intervals for the complete annotation set. | Skill policy training and data analysis |
| `skill_eval_configs/` | Episode-level YAML files containing BDDL context and a success goal for every evaluated skill. | Independent skill evaluation |
| `skill_init_states/` | Restorable intermediate OmniGibson states and per-episode manifests for the evaluation set. | Initializing skill-level rollouts under valid preconditions |
| `eval_episodes.json` | Mapping from the 50 tasks to their prompts, task IDs, and 10 evaluation episodes. | Resolving the evaluation subset and file paths |

The annotations cover the complete demonstration set. Evaluation configurations and initial states cover only the 500 evaluation demonstrations, not all 235,492 annotated skill instances.

## Directory Structure

```text
Behavior-Skill/
├── README.md
├── LICENSE
├── CITATION.cff
├── eval_episodes.json
├── skill_annotations/
│   ├── task-0000/
│   │   ├── episode_00000010_step.json
│   │   └── ...
│   └── task-0049/
├── skill_eval_configs/
│   ├── turning_on_radio/
│   │   ├── episode_00000010.yaml
│   │   └── ...
│   └── ...
└── skill_init_states/
    ├── task-0000/
    │   ├── episode_00000010/
    │   │   ├── skill_00_scene.json
    │   │   ├── skill_00_state.npz
    │   │   ├── ...
    │   │   └── state_manifest.json
    │   └── ...
    └── task-0049/
```


## Data Formats

### Skill annotations

Each `skill_annotations/task-XXXX/episode_XXXXXXXX_step.json` file describes the ordered constituent skills in one demonstration.

| Field | Description |
| --- | --- |
| `task` | Task name |
| `task_description` | Task-level instruction |
| `cot_skill_list` | Ordered skill instructions |
| `cot_skill_frame_duration` | Ordered start and end frames aligned one-to-one with `cot_skill_list` |

### Evaluation configurations

Each `skill_eval_configs/<task_name>/episode_XXXXXXXX.yaml` file contains task-level BDDL context and an ordered `skills` list.

| Field | Description |
| --- | --- |
| `task_name` | Underscore-separated task name |
| `activity_definition_id` | BEHAVIOR-1K activity definition ID |
| `objects_bddl` | Typed BDDL object declarations |
| `init_bddl` | Relevant initial BDDL facts |
| `extra_object_name_map` | Optional BDDL-to-scene mappings for objects absent from the original task BDDL |
| `skills` | Skill-level instructions, categories, types, and BDDL goals |

Each skill entry contains `id`, `instruction`, `skill_description`(the semantic skill category), `type`, and `bddl_goal`. 

### Skill initial states

Each evaluation episode has a directory under `skill_init_states/task-XXXX/episode_XXXXXXXX/`.

| File | Description |
| --- | --- |
| `skill_XX_scene.json` | Restorable OmniGibson scene snapshot for the state immediately before skill `XX`. |
| `skill_XX_state.npz` | Alignment metadata containing `skill`, `start_frame`, and `end_frame`. |
| `state_manifest.json` | Maps every skill index and frame interval to its matching scene and NPZ files and records the source HDF5 path. |

State restoration is version-sensitive. The snapshots were generated with OmniGibson 3.7.2, BDDL 3.7.0, and BEHAVIOR-1K assets 3.7.2rc1; follow the environment specified by the accompanying code release.

## Cross-Component Mapping

Task ID, episode ID, and local skill index connect the three components. For example, `task-0000`, episode `00000010`, skill `02` maps to:

| Component | Location |
| --- | --- |
| Annotation | `skill_annotations/task-0000/episode_00000010_step.json` → `cot_skill_list[2]` |
| BDDL goal | `skill_eval_configs/turning_on_radio/episode_00000010.yaml` → `skills[2]` |
| State manifest | `skill_init_states/task-0000/episode_00000010/state_manifest.json` → `skills[2]` |
| State files | `skill_02_scene.json` and `skill_02_state.npz` in the corresponding episode directory |


## Download

```bash
pip install -U huggingface_hub

hf download mafangniu/Behavior-Skill \
  --repo-type dataset \
  --local-dir ./Behavior-Skill
```

To download a single component:

```bash
hf download mafangniu/Behavior-Skill \
  --repo-type dataset \
  --include "skill_eval_configs/**" \
  --local-dir ./Behavior-Skill
```

Use a tagged release or fixed Hub revision for reproducible experiments.


## License

The original Behavior-Skill annotations, evaluation configurations, state snapshots, metadata, and documentation are released under the MIT License. Third-party datasets, simulators, and assets remain subject to their respective licenses and terms. See [LICENSE](LICENSE) for details.

## Citation

If you find Behavior-Skill useful in your research, please cite our paper:
```bibtex
@article{ma2026behaviorskill,
  title   = {{Behavior-Skill}: A Fine-Grained Benchmark for Evaluating Vision-Language-Action Policies in Long-Horizon Tasks},
  author  = {Ma, Chunyun and Luo, Lun and Luo, Xingjian and Feng, Xiexing and Zhang, Hang and Liu, Wei and Qiao, Feng and Wang, Yaonan and Lu, Huimin and Chen, Xieyuanli},
  journal = {arXiv preprint arXiv:2608.30536},
  year    = {2026},
  url     = {https://arxiv.org/abs/2608.30536}
}
```

Please also cite the original BEHAVIOR-1K benchmark:

```bibtex
@inproceedings{li2023behavior,
  title={Behavior-1k: A benchmark for embodied ai with 1,000 everyday activities and realistic simulation},
  author={Li, Chengshu and Zhang, Ruohan and Wong, Josiah and Gokmen, Cem and Srivastava, Sanjana and Mart{\'\i}n-Mart{\'\i}n, Roberto and Wang, Chen and Levine, Gabrael and Lingelbach, Michael and Sun, Jiankai and others},
  booktitle={Conference on Robot Learning},
  pages={80--93},
  year={2023},
  organization={PMLR}
}
```

## Contact

For questions or reproducible data issues, open an issue in the Behavior-Skill code repository or contact `Chunyun Ma` at `mafangniu@gmail.com`.