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

This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).

## Dataset Description

This dataset provides the robotic trajectories and observations used in 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, enabling faster inference for robotic manipulation tasks. The datasets are built on [LeRobot](https://github.com/huggingface/lerobot) Hugging Face formats and optimized into offline `zarr` for faster training.

- **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](meta/info.json):
```json
{
    "codebase_version": "v2.1",
    "robot_type": null,
    "total_episodes": 175,
    "total_frames": 26266,
    "total_tasks": 1,
    "total_videos": 350,
    "total_chunks": 1,
    "chunks_size": 1000,
    "fps": 20,
    "splits": {
        "train": "0:175"
    },
    "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.fps": 20.0,
                "video.height": 256,
                "video.width": 256,
                "video.channels": 3,
                "video.codec": "av1",
                "video.pix_fmt": "yuv420p",
                "video.is_depth_map": false,
                "has_audio": false
            }
        },
        "observation.images.robot0_eye_in_hand_image": {
            "dtype": "video",
            "shape": [
                256,
                256,
                3
            ],
            "names": [
                "height",
                "width",
                "channel"
            ],
            "info": {
                "video.fps": 20.0,
                "video.height": 256,
                "video.width": 256,
                "video.channels": 3,
                "video.codec": "av1",
                "video.pix_fmt": "yuv420p",
                "video.is_depth_map": false,
                "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

This dataset is designed to be used with the VITA codebase, which extends [LeRobot](https://github.com/huggingface/lerobot). Below are examples for converting datasets to an optimized `zarr` format and training a VITA policy.

First, ensure the VITA repository is cloned and setup, and the `FLARE_DATASETS_DIR` environment variable is set as described in the [VITA GitHub repository](https://github.com/ucd-dare/VITA#%EF%B8%8F-setup).

### Dataset Preprocessing

To list available datasets:
```bash
cd gym-av-aloha/scripts
python convert.py --ls
```

To convert a HuggingFace dataset to an offline `zarr` format (e.g., `av_aloha_sim_hook_package`):
```bash
python convert.py -r iantc104/av_aloha_sim_hook_package
```

### Training a VITA Policy

Once the dataset is converted, you can train a VITA policy using the `flare` module from the VITA codebase:
```bash
python flare/train.py policy=vita task=hook_package session=test
```
You can override default configurations as needed:
```bash
# Example: Use a specific GPU
python flare/train.py policy=vita task=hook_package session=test device=cuda:2

# Example: Change online validation frequency and episodes
python flare/train.py policy=vita task=hook_package session=test \
  val.val_online_freq=2000 val.eval_n_episodes=10

# Example: Run an ablation
python flare/train.py policy=vita task=hook_package session=ablate \
  policy.vita.decode_flow_latents=False wandb.notes=ablation
```

## Citation

**BibTeX:**

```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}
}
```