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tencent/
Hy-Embodied-0.5-VLA-RoboTwin

Name: Hy-Embodied-0.5-VLA-RoboTwin
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tencent/Hy-Embodied-0.5-VLA-UMI

finetune

apache-2.0

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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-VLA-RoboTwin is the supervised fine-tuned (SFT) checkpoint of Hy-Embodied-0.5-VLA (Hy-VLA), an end-to-end Vision-Language-Action system built on the Hy-Embodied-0.5 MoT backbone. Fine-tuned on all 50 bimanual manipulation tasks in the RoboTwin 2.0 benchmark, it achieves 90.9% (Clean) / 90.1% (Randomized) average success rate — state-of-the-art among published VLA methods.

Architecture

Same as Hy-Embodied-0.5-VLA-UMI, with the following SFT-specific settings:

Video Encoder: K=6 frames (current + 5 historical), temporal-spatial attention enabled
Action Horizon: H=20 at 3× downsampled rate (effective horizon covers a longer time span)
All other architecture parameters identical to the pre-trained checkpoint

Training

Property	Value
Data	RoboTwin 2.0: 50 tasks × 550 episodes (50 clean + 500 randomized)
Initialization	`tencent/Hy-Embodied-0.5-VLA-UMI`
Objective	Conditional flow matching
Global batch size	128
Learning rate	5e-5 (warmup 1K → cosine decay to 5e-6 over 150K)
Optimizer	AdamW, bfloat16 mixed precision
Hardware	32 GPUs (4 nodes × 8)

Evaluation Performance (RoboTwin 2.0)

Setting	Success Rate
Clean	90.9%
Randomized	90.1%

tencent/Hy-Embodied-0.5-VLA-RoboTwin/
├── model.safetensors         # Model weights
├── config.json               # HyVLA configuration
├── tokenizer.json            # Tokenizer for the VLM backbone
├── tokenizer_config.json
├── special_tokens_map.json
├── chat_template.jinja       # Chat template for instruction formatting
├── preprocessor_config.json  # Image preprocessing config
├── norm_stats.pkl            # Pre-computed normalization statistics
└── LICENSE

Usage

Basic Loading

python

import torch
from huggingface_hub import snapshot_download
from hy_vla import HyVLA, HyVLAConfig

ckpt = snapshot_download("tencent/Hy-Embodied-0.5-VLA-RoboTwin")

config = HyVLAConfig.from_pretrained(ckpt)
policy = HyVLA.from_pretrained(ckpt, config=config)
policy.enable_video_encoder_if_needed()
policy = policy.to(device="cuda", dtype=torch.bfloat16).eval()

# (B, K, C, H, W); K=6 history slots
img = torch.zeros(1, 6, 3, 224, 224, device="cuda", dtype=torch.bfloat16)
# Normalized dual-arm EEF: [xyz(3) + rot6d(6) + gripper(1)] * 2
state = torch.zeros((1, config.max_state_dim), device="cuda", dtype=torch.bfloat16)
batch = {
    "observation.images.top_head":   img,
    "observation.images.hand_left":  img,
    "observation.images.hand_right": img,
    "observation.state": state,
    "task": ["pick up the bottle"],
}

with torch.no_grad():
    actions = policy.forward_evaluate(batch)["pred"]
    actions = actions[..., : config.action_feature.shape[0]]
print(actions.shape)

RoboTwin Evaluation

To reproduce the benchmark results:

bash

export ROBOTWIN_DIR=/path/to/RoboTwin
export CKPT_PATH=tencent/Hy-Embodied-0.5-VLA-RoboTwin

# Quick regression (6 tasks × 10 rollouts)
bash scripts/eval_robotwin_test.sh

# Full sweep (50 tasks × 100 rollouts, 8 GPUs)
bash scripts/eval_robotwin_full.sh

Note: The eval scripts automatically symlink Hy-VLA/robotwin_eval/ → RoboTwin/policy/hy_vla, so that RoboTwin's eval_policy.py can discover the Hy-VLA policy adapter without any manual configuration.

Normalization Statistics

The checkpoint includes pre-computed norm_stats.pkl from the RoboTwin 2.0 training data. For fine-tuning on new robot platforms, regenerate using:

bash

python scripts/compute_norm_hdf5.py \
    --csv /path/to/episodes.csv \
    --hdf5-dir /path/to/hdf5 \
    --output norm_stats.pkl

📚 Citation

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

bibtex

@article{zhang2026hy,
  title={Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack},
  author={Zhang, He and Xiang, Lingzhu and Lin, Haitao and Huang, Zeyu and Wang, Minghui and Zhong, Dingyan and Dong, Yubo and Wu, Yihao and Rao, Yongming and Zhang, Dongsheng and others},
  journal={arXiv preprint arXiv:2606.14409},
  year={2026}
}

License

This model is released under Apache-2.0.
The base model tencent/Hy-Embodied-0.5-VLA-UMI is also released under Apache-2.0.

tencent/Hy-Embodied-0.5-VLA-RoboTwin