README.md

---
license: apache-2.0
task_categories:
  - robotics
  - video-classification
language:
  - en
tags:
  - robotics
  - humanoid
  - GR1
  - LeRobot
  - video-to-action
  - synthetic-data
  - cosmos-predict2
  - dreamgen
pretty_name: Vi2Act
size_categories:
  - n<1K
---

# Vi2Act

**Vi2Act** is a small synthetic video-to-action dataset for the Fourier **GR1** humanoid.
Videos are generated by a Cosmos-Predict2 14B Video2World model (GR00T-Dreams-GR1 checkpoint), then converted into [LeRobot](https://github.com/huggingface/lerobot) format.

Source pipeline: [NVIDIA GR00T-Dreams / DreamGen](https://github.com/NVIDIA/GR00T-Dreams) using the [EVAL-175](https://huggingface.co/datasets/nvidia/EVAL-175) DreamGen Bench prompts and first-frame images.

## What's in this repo

Three LeRobot subsets (5 episodes each):

| Folder | Split (EVAL-175) | Episodes | Frames | FPS | Tasks |
|--------|------------------|----------|--------|-----|-------|
| `gr1_env_lerobot.data/` | environment generalization | 5 | 465 | 8 | 5 |
| `gr1_object_lerobot.data/` | object generalization | 5 | 465 | 8 | 5 |
| `gr1_behavior_lerobot.data/` | behavior / skill generalization | 5 | 465 | 8 | 5 |

**Totals:** 15 episodes, 1,395 frames, 15 language tasks, 1 ego-view camera.

This is a **pilot / smoke-test** dump (`NUM_SAMPLES=5` per subset), not the full EVAL-175 benchmark.

## Embodiment

- Robot: Fourier GR1 (`robot_type: dream` in `meta/info.json`)
- Action / state dim: **44** joints
- Camera: `observation.images.ego_view`

Joint layout (`meta/modality.json`):

| Group | Indices |
|-------|---------|
| left_arm | 0–6 |
| left_hand | 7–12 |
| left_leg | 13–18 |
| neck | 19–21 |
| right_arm | 22–28 |
| right_hand | 29–34 |
| right_leg | 35–40 |
| waist | 41–43 |

## Directory layout (per subset)

```
{subset}_lerobot.data/
├── data/chunk-000/episode_XXXXXX.parquet
├── videos/chunk-000/observation.images.ego_view/episode_XXXXXX.mp4
└── meta/
    ├── info.json
    ├── episodes.jsonl
    ├── tasks.jsonl
    ├── modality.json
    └── stats.json
```

Each parquet row stores:

- `observation.state` — `float32[44]`
- `action` — `float32[44]`
- `annotation.human.coarse_action` — language-task index
- video frames referenced via LeRobot video paths

> **Note on actions.** Videos and language labels come from Cosmos generation. The 44-D `action` / `state` columns follow the GR1 LeRobot schema. Inverse-dynamics (IDM) action labels (`seonghyeonye/IDM_gr1`) are intended as a follow-up upload under `*_lerobot.data_idm/`. Until that dump is published, treat action columns as schema placeholders unless you re-run IDM locally.

## Language tasks

### `gr1_env`

1. Use the right hand to pick up chip with green packaging to white bag
2. Use the right hand to pick up orange to metal platform
3. Use the right hand to pick up plastic pitcher and pour water onto green plant
4. Use the right hand to pick up the spoon from the bowl and serve the contents onto the plate
5. Use the right hand to knock over the green Pocky box

### `gr1_object`

1. Use the left hand to pick up dark green cucumber from on circular gray mat to above beige bowl.
2. Use the right hand to pick up red bell pepper from center of tan countertop to brown bottom rack.
3. Use the left hand to pick up green pepper from tan table, below the bright blue plate to pale turquoise plate.
4. Use the right hand to pick up milk carton from pale turquoise plate in the center of the table to second level of wooden shelf.
5. Use the right hand to pick up green bok choy from top tier of two tier black and white shelf to brown paper bag.

### `gr1_behavior`

1. Use the right hand to close lunch box
2. Use the right hand to pick up blue scoop and scoop powder from container
3. Use the right hand to pick up glass and bring it close to the camera as if drinking
4. Use knife to cut the object on the cutting board
5. Use the right hand to press calculator

## How the data was produced

1. **World-model videos** — Cosmos-Predict2-14B Sample GR00T-Dreams-GR1, image + text conditioning, `--disable_guardrail`, 480p / 16 fps generation then resampled to 8 fps / 93 frames for Cosmos-Predict2 LeRobot packing.
2. **Directory conversion** — DreamGen Bench filenames → per-task folders.
3. **LeRobot export** — `IDM_dump/raw_to_lerobot.py --cosmos_predict2`.

## Load with Hugging Face Hub

```python
from huggingface_hub import snapshot_download

local_dir = snapshot_download(
    repo_id="hk239/Vi2Act",
    repo_type="dataset",
)
# e.g. local_dir/gr1_env_lerobot.data/
```

## Load as a LeRobot dataset

```python
from gr00t.data.dataset import LeRobotSingleDataset

dataset = LeRobotSingleDataset(
    dataset_path="gr1_env_lerobot.data",  # after download
    embodiment_tag="gr1",
)
```

## Citation

If you use this data, please cite DreamGen / GR00T-Dreams:

```bibtex
@article{jang2025dreamgen,
  title={DreamGen: Unlocking Generalization in Robot Learning through Video World Models},
  author={Jang, Joel and Ye, Seonghyeon and Lin, Zongyu and Xiang, Jiannan and Bjorck, Johan and Fang, Yu and Hu, Fengyuan and Huang, Spencer and Kundalia, Kaushil and Lin, Yen-Chen and others},
  journal={arXiv preprint arXiv:2505.12705v2},
  year={2025}
}
```

## Acknowledgements

- [nvidia/EVAL-175](https://huggingface.co/datasets/nvidia/EVAL-175) — prompt / first-frame images
- [nvidia/Cosmos-Predict2-14B-Sample-GR00T-Dreams-GR1](https://huggingface.co/nvidia/Cosmos-Predict2-14B-Sample-GR00T-Dreams-GR1) — video world model
- [seonghyeonye/IDM_gr1](https://huggingface.co/seonghyeonye/IDM_gr1) — inverse dynamics model (for action extraction)