tencent/Hy-Embodied-0.5-VLA-Data

Hy-Embodied-0.5-VLA

From Vision-Language-Action Models to a Real-World Robot Learning Stack

Tencent Robotics X × Tencent Hy Team

📖 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 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 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 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):

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 (LeRobotLanceDataset).

Episode Visualization

# 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:

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:

@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.