# WatchAct: A Benchmark for Behavior-Grounded Robot Manipulation

🌐 [Project Page](https://baiqi-li.github.io/watchact_page/)  |  🤗 [Dataset](https://huggingface.co/datasets/BaiqiL/WatchAct)  |  💻 [Code](https://github.com/Baiqi-Li/WatchAct)

WatchAct is a behavior-grounded benchmark for robotic manipulation, where the robot reasons over observed human behavior and a language instruction to perform the corresponding task.

## Files

```
.
├── data/<task>.jsonl          # evaluation instances (one row per video × view × variant)
├── meta_data/<task>.json      # ground-truth scene definition, initial states, and goal
├── videos/<task>/             # multi-view recordings: front/, side/, oblique/ (*.MP4)
└── bddl_files/<task>/         # LIBERO task definition files (*.bddl)
```

- **`data/`** — the model-facing evaluation rows. Each row provides a
  `language_instruction`, the `video` to watch, its `camera_perspective` /
  `spatial_reference`, and links back to its `meta_data` example via
  `original_id` and to its task file via `bddl_file`.
- **`meta_data/`** — per-example ground truth: the object list, symbolic
  `initial_states`, the target `final_goal`, and the `oracle_plan` used for
  scoring.
- **`videos/`** — three synchronized views (`front`, `side`, `oblique`) of a
  person performing each task.
- **`bddl_files/`** — the corresponding LIBERO simulation task definitions.

A `<task>` is the file stem (e.g. `Ordinal`, `Fine-Grained_Action`). The 14
tasks, grouped by capability:

| Category | Tasks |
|---|---|
| Procedural Reasoning | `Imitation`, `Reversal`, `Temporal_Sort` |
| Event Grounding | `Fine-Grained_Action`, `Count`, `Ordinal`, `State_Change`, `Moment` |
| Implicit Intent Inference | `Nonverbal_Cue`, `Reference_Disambiguation` |
| Episodic Reasoning | `Restore_Previous_State`, `Task_Continuation`, `Error_Correction`, `Conditional_Execution` |

See the [code repository](https://github.com/Baiqi-Li/WatchAct) for the full
evaluation pipeline.