🌐 UniVLA: Unified Vision-Language-Action Model

A general-purpose VLA Model designed to unify vision, language, and action for robotics and autonomous driving.

📜 [technical report] 🤗 [model weights] 🤖 [project page]

🚀 News

• 2025.10.15: DriveVLA-W0, extended the UniVLA framework to autonomous driving, code released.
• 2025.6.27: code released for robotic simulations.
• 2025.6.25: paper released on the arXiv.

🧪 Highlights

• Unified Vision-Language-Action Model: supports image grounding, video generation, and action prediction.
• Strong Performance on Several Robotics Benchmarks: support CALVIN, LIBERO, SimplerEnv.
• Interleaved Video Training: support interleaved vision-action training in Markov Decision Process.
• Broader Applications: Real-robot ALOHA & Autonomous Driving.

🔧 REPO TODO List

• Policy learning for CALVIN, LIBERO, and SimplerEnv.
• Support for evaluation.
• World model pretraining for video generation.
• Example for real-robot ALOHA.
• Support for autonomous driving.
• Support for general grounding.

📚 Experiments

Emu3 Pretraining Models

You can download the pretraining models from HuggingFace, here we provide the links.

Emu3-base

Emu3-vision

World Model Training

More details can be found in the World Model Training document.

# train the world model
bash scripts/pretrain/train_video_1node.sh

world model pretraining ckpts

This model is used to serve as the prerained model for the downstream policy learning tasks, such as CALVIN, LIBERO, and SimplerEnv.

1. CALVIN Benchmark

Note: 5× means 5× inference steps, i.e., 180 steps total.

Training

• Here provide single node training script, recommend multi-node training.

# video sft
bash scripts/simulator/calvin/train_calvin_abcd_video.sh

2. LIBERO Benchmark

Training

bash scripts/simulator/libero/train_libero_video.sh

3. SimplerEnv Benchmark

Training

bash scripts/simulator/simplerenv/train_simplerenv_bridge_video.sh

Setup

Here we provide a conda environment setup for the project.

conda create -n emu_vla python=3.10
pip install -r requirements.txt

Benchmark setup, training and evaluation

• CALVIN
• LIBERO
• SimplerEnv
• ALOHA

OmniSim/
├── configs/       # Model configuration files
├── models/        # Tokenizer and diffusion test
├── train/         # Training dataset and pipeline
├── reference/     # Reference code
│   ├── Emu3/      # Base code
│   └── RoboVLMs/  # Evaluation code
├── scripts/       # Shell scripts for training & evaluation
├── tools/         # Data preprocessing tools
└── README.md      # Project description and user guide
    

❤️ Acknowledgement

Our work is built upon the following projects, Thanks for their great open-source work!

• Emu3
• RoboVLMs
• OpenVLA

🌟Citation

If you find this project useful, please consider citing our work:

@article{wang2025unified,
  title={Unified Vision-Language-Action Model},
  author={Wang, Yuqi and Li, Xinghang and Wang, Wenxuan and Zhang, Junbo and Li, Yingyan and Chen, Yuntao and Wang, Xinlong and Zhang, Zhaoxiang},
  journal={arXiv preprint arXiv:2506.19850},
  year={2025}
}