README.md

metadata

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. It contains data associated with the paper VITA: Vision-to-Action Flow Matching Policy.

Dataset Description

This dataset is associated with the paper VITA: Vision-to-Action Flow Matching Policy. 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/
• Paper: https://huggingface.co/papers/2507.13231
• Code: https://github.com/ucd-dare/VITA
• License: apache-2.0

Dataset Structure

meta/info.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 for optimized preprocessing and training.

First, set up the VITA environment as described in the Github repository:

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:

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:

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:

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:

python flare/train.py policy=vita task=hook_package session=test

Citation

@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}
}