README.md

---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- flow-matching
configs:
- config_name: default
  data_files: data/*/*.parquet
---

This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). It contains data associated with the paper [VITA: Vision-to-Action Flow Matching Policy](https://huggingface.co/papers/2507.13231).

## Dataset Description

This dataset is associated with the paper [VITA: Vision-to-Action Flow Matching Policy](https://huggingface.co/papers/2507.13231). VITA introduces a noise-free and conditioning-free policy learning framework that directly maps visual representations to latent actions using flow matching. This dataset comprises the data used for evaluating VITA on 8 simulation and 2 real-world tasks from ALOHA and Robomimic.

- **Homepage:** [https://ucd-dare.github.io/VITA/](https://ucd-dare.github.io/VITA/)
- **Paper:** [https://huggingface.co/papers/2507.13231](https://huggingface.co/papers/2507.13231)
- **Code:** [https://github.com/ucd-dare/VITA](https://github.com/ucd-dare/VITA)
- **License:** apache-2.0

## Dataset Structure

[meta/info.json](meta/info.json):
```json
{
    "codebase_version": "v2.1",
    "robot_type": null,
    "total_episodes": 192,
    "total_frames": 22305,
    "total_tasks": 1,
    "total_videos": 384,
    "total_chunks": 1,
    "chunks_size": 1000,
    "fps": 20,
    "splits": {
        "train": "0:192"
    },
    "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
    "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
    "features": {
        "action": {
            "dtype": "float32",
            "shape": [
                7
            ],
            "names": null
        },
        "action.delta": {
            "dtype": "float32",
            "shape": [
                7
            ],
            "names": null
        },
        "action.absolute": {
            "dtype": "float32",
            "shape": [
                7
            ],
            "names": null
        },
        "observation.state": {
            "dtype": "float32",
            "shape": [
                43
            ],
            "names": null
        },
        "observation.environment_state": {
            "dtype": "float32",
            "shape": [
                14
            ],
            "names": null
        },
        "observation.images.agentview_image": {
            "dtype": "video",
            "shape": [
                256,
                256,
                3
            ],
            "names": [
                "height",
                "width",
                "channel"
            ],
            "info": {
                "video.height": 256,
                "video.width": 256,
                "video.codec": "av1",
                "video.pix_fmt": "yuv420p",
                "video.is_depth_map": false,
                "video.fps": 20,
                "video.channels": 3,
                "has_audio": false
            }
        },
        "observation.images.robot0_eye_in_hand_image": {
            "dtype": "video",
            "shape": [
                256,
                256,
                3
            ],
            "names": [
                "height",
                "width",
                "channel"
            ],
            "info": {
                "video.height": 256,
                "video.width": 256,
                "video.codec": "av1",
                "video.pix_fmt": "yuv420p",
                "video.is_depth_map": false,
                "video.fps": 20,
                "video.channels": 3,
                "has_audio": false
            }
        },\
        "timestamp": {
            "dtype": "float32",
            "shape": [
                1
            ],
            "names": null
        },
        "frame_index": {
            "dtype": "int64",
            "shape": [
                1
            ],
            "names": null
        },
        "episode_index": {
            "dtype": "int64",
            "shape": [
                1
            ],
            "names": null
        },
        "index": {
            "dtype": "int64",
            "shape": [
                1
            ],
            "names": null
        },
        "task_index": {
            "dtype": "int64",
            "shape": [
                1
            ],
            "names": null
        }
    }
}
```

## Sample Usage

The datasets are designed to be used with the VITA codebase, which extends [LeRobot](https://github.com/huggingface/lerobot) for optimized preprocessing and training.

First, set up the VITA environment as described in the [Github repository](https://github.com/ucd-dare/VITA):
```bash
git clone git@github.com:ucd-dare/VITA.git
cd VITA
conda create --name vita python==3.10
conda activate vita
conda install cmake
pip install -e .
pip install -r requirements.txt
# Install LeRobot dependencies
cd lerobot
pip install -e .
# Install ffmpeg for dataset processing
conda install -c conda-forge ffmpeg
```

Set the dataset storage path:
```bash
echo 'export FLARE_DATASETS_DIR=<PATH_TO_VITA>/gym-av-aloha/outputs' >> ~/.bashrc
# Reload bashrc
source ~/.bashrc
conda activate vita
```

You can list available datasets (hosted on Hugging Face) using the conversion script:
```bash
cd gym-av-aloha/scripts
python convert.py --ls
```

To convert a Hugging Face dataset to the optimized offline Zarr format for faster training (this may take >10 minutes), for example:
```bash
python convert.py -r iantc104/av_aloha_sim_hook_package
```
Converted datasets will be stored in the path specified by `FLARE_DATASETS_DIR`.

To train a policy using a task (e.g., `hook_package`) with the VITA framework:
```bash
python flare/train.py policy=vita task=hook_package session=test
```

## Citation

```bibtex
@article{gao2025vita,
  title={VITA: Vision-to-Action Flow Matching Policy},
  author={Gao, Dechen and Zhao, Boqi and Lee, Andrew and Chuang, Ian and Zhou, Hanchu and Wang, Hang and Zhao, Zhe and Zhang, Junshan and Soltani, Iman},
  journal={arXiv preprint arXiv:2507.13231},
  year={2025}
}
```