---
license: cc-by-4.0
task_categories:
- robotics
- reinforcement-learning
tags:
- robotics
- manipulation
- bimanual
- VLA
- leRobot
- lance
- imitation-learning
pretty_name: Hy-Embodied-0.5-VLA-Data
size_categories:
- 1M<n<10M
---

<div align="center">
<h1>Hy-Embodied-0.5-VLA</h1>
<p><b>From Vision-Language-Action Models to a Real-World Robot Learning Stack</b></p>
<p><i>Tencent Robotics X × Tencent Hy Team</i></p>

<a href="https://tairos.tencent.com/openSourceModels/hy-embodied-0.5-vla"><img src="https://img.shields.io/badge/Website-Project_Page-blue?logo=internet-explorer" alt="Project Page"></a>
<a href="https://arxiv.org/abs/2606.14409"><img src="https://img.shields.io/badge/PDF-arXiv-red?logo=arxiv" alt="Tech Report"></a>
<a href="https://huggingface.co/tencent/Hy-Embodied-0.5-VLA-UMI"><img src="https://img.shields.io/badge/Model-HuggingFace-yellow?logo=huggingface" alt="Model"></a>
<a href="https://huggingface.co/datasets/tencent/Hy-Embodied-0.5-VLA-Data"><img src="https://img.shields.io/badge/Data-HuggingFace-orange?logo=huggingface" alt="Data"></a>
<a href="https://github.com/Tencent-Hunyuan/Hy-Embodied-0.5-VLA"><img src="https://img.shields.io/badge/Code-GitHub-black?logo=github" alt="Code"></a>
</div>

<div align="center">
<video src="https://github.com/user-attachments/assets/fdd1966c-8453-4f6a-9758-238076d08ac4" controls autoplay muted loop width="85%"></video>
</div>

## 📖 Abstract

We introduce **Hy-Embodied-0.5-VLA (Hy-VLA)** — an end-to-end Vision-Language-Action system that spans the full robot learning stack: data collection, model design, pre-training, supervised fine-tuning, RL post-training, and real-world deployment. Built on the [Hy-Embodied-0.5](https://github.com/Tencent-Hunyuan/HY-Embodied) MoT backbone, Hy-VLA integrates a flow-matching action expert, a compact memory encoder for multi-frame history, and a delta-chunk action representation decoupled from embodiment-specific kinematics.

Powered by **10,000+ hours** of high-fidelity UMI demonstrations collected via a custom fingertip interface with optical motion-capture, Hy-VLA achieves state-of-the-art results on the RoboTwin 2.0 benchmark (**90.9% / 90.1%** on Clean / Randomized) and demonstrates robust cross-embodiment transfer across four real-world robot platforms. Paired with [FlowPRO](https://wuyeyexvnainai.github.io/flowpro/) preference optimization and an asynchronous inference framework, Hy-VLA establishes a scalable paradigm for continuous dexterous manipulation.


## Overview

Hy-Embodied-0.5-VLA-Data is a large-scale bimanual manipulation dataset for training Vision-Language-Action (VLA) foundation models. Powered by **2000+ hours** of high-fidelity demonstrations collected via a custom fingertip UMI device with optical motion-capture, it spans 70+ manipulation tasks. The dataset is released in **Lance** format compatible with [LeRobot](https://github.com/huggingface/lerobot) v3.0.

> **Note**: The open-source release contains approximately **20%** of the full corpus.

## Dataset Statistics

| Property | Value |
|---|---|
| Total Episodes | 250,304 |
| Total Frames | 233,600,314 |
| Total Duration | 2,163 hours |
| Total Size | ~18.8 TB (22 tables) |
| Frequency | 30 Hz |
| Cameras | 3 views (head + left wrist + right wrist) |
| Resolution | 240 × 424 px per camera |
| Format | Lance (LeRobot v3.0 schema) |
| Tables | 22 (table_000 ~ table_021, ~100h each) |

## Directory Structure

Each table is a self-contained LeRobot v3.0 dataset root:

```
table_000/
├── table_000.lance/          # Lance columnar data (5GB shards)
│   ├── _versions/
│   └── data/
│       └── ...-data-0.lance
└── meta/                     # LeRobot v3.0 metadata
    ├── info.json             # Table-level summary
    ├── stats.json            # Per-feature statistics
    ├── tasks.parquet         # Task ↔ index mapping
    └── episodes/             # Episode boundary parquet files
```

## Data Schema

Each row (frame) contains:

### Observations

| Column | Type | Shape | Description |
|---|---|---|---|
| `observation.state` | `float32` | `[16]` | Dual-arm EEF state: left [x,y,z,qx,qy,qz,qw,gripper], right [x,y,z,qx,qy,qz,qw,gripper] |
| `observation.images.cam_high` | `image` | `[240,424,3]` | Overhead camera RGB image |
| `observation.images.cam_left_wrist` | `image` | `[240,424,3]` | Left wrist-mounted camera RGB image |
| `observation.images.cam_right_wrist` | `image` | `[240,424,3]` | Right wrist-mounted camera RGB image |

### Actions

| Column | Type | Shape | Description |
|---|---|---|---|
| `action` | `float32` | `[2]` | Gripper openness [left, right] derived from state |

### Metadata

| Column | Type | Shape | Description |
|---|---|---|---|
| `task_index` | `int32` | `[1]` | Task ID mapping to `meta/tasks.parquet` |
| `task` | `string` | `[1]` | Task description (Chinese, e.g., "抓取红色方块并放入盒子") |
| `episode_index` | `int32` | `[1]` | Global unique episode index |
| `frame_index` | `int32` | `[1]` | Frame index within episode (starts at 0) |
| `timestamp` | `float32` | `[1]` | Seconds from episode start |

## Usage

`LanceTableReader` reads a single Lance table (local or HF Hub):

```python
from hy_vla.data.lance_dataset import LanceTableReader

# Local directory
reader = LanceTableReader(root="./table_000")

# HF Hub
reader = LanceTableReader(
    repo_id="tencent/Hy-Embodied-0.5-VLA-Data",
    table_name="table_000",
)

# Access
frame = reader[42]                        # single frame dict
episode = reader.get_episode(3)           # all frames of episode 3
```

> Also compatible with raw `lance`, `lancedb`, and [`lerobot-lancedb`](https://github.com/lancedb/lerobot-lancedb) (`LeRobotLanceDataset`).

### Episode Visualization

```bash
# Use the HF Hub dataset, pick table_000 episode 666
python scripts/vis_umi_episode.py -t table_000 -e 666

# Local Lance root
python scripts/vis_umi_episode.py /path/to/Hy-Embodied-0.5-Data -e 0 --no-3d
```

### Downloading Specific Tables

Due to the large total size (~18.8 TB), you may prefer to download individual tables:

```python
from huggingface_hub import snapshot_download

# Download only table_000 (~890 GB)
snapshot_download(
    "tencent/Hy-Embodied-0.5-VLA-Data",
    allow_patterns="table_000/**",
    repo_type="dataset"
)
```

## 📚 Citation

If you find Hy-VLA useful for your research, please cite:

```bibtex
@article{tencent2026hyembodied05vla,
  title={Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack},
  author={Tencent Robotics X and Tencent Hy Team},
  journal={arXiv preprint arXiv:2606.14409},
  year={2026}
}
```

## License

This dataset is released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).