One-Stop VLA Development Toolbox for Embodied Intelligence

Pretraining · Fine-tuning · Inference · Evaluation
Supporting mainstream policies such as π0, CogACT, OFT, MemVLA, and more

Dexbotic is a VLA (Vision-Language-Action) development toolbox built on the PyTorch framework, designed to provide a unified and efficient solution for embodied intelligence research. It comes with built-in environment configurations for various mainstream VLA models, allowing users to reproduce, fine-tune, and inference cutting-edge VLA algorithms with simple setup.

• Ready-to-Use VLA Framework: Centered around VLA models, integrating embodied manipulation and navigation capabilities, supporting multiple cutting-edge algorithms.
• High-Performance Pre-trained Foundation Models: For mainstream VLA algorithms such as π0 and CogACT, Dexbotic provides multiple optimized pre-trained models.
• Modular Development Architecture: Adopting a "layered configuration + factory registration + entry dispatch" architecture, users can easily modify configurations, change models, or add tasks by simply modifying experimental scripts.
• Unified Cloud and Local Training: Fully supports both cloud and local training needs, supporting cloud training platforms such as Alibaba Cloud and Volcano Engine, while also accommodating consumer-grade GPUs for local training.
• Extensive Robot Compatibility: For mainstream robots such as UR5, Franka, and ALOHA, Dexbotic provides a unified training data format and deployment scripts.

• [2026-01-15] Released a tutorial on integrating SO-101 with Dexbotic.
• [2026-01-15] Supported SimpleVLA-RL
• [2026-01-15] Supported NaVILA.
• [2026-01-08] Added Co-training capability, enabling joint optimization of action experts and LLMs for the CogACT model.
• [2026-01-08] Released a specialized image compatible with Blackwell GPUs.
• [2025-12-29] Supported OFT and Pi0.5 models.
• [2025-10-20] Dexbotic officially released! Check out the technical report and official documentation for details.

We strongly recommend using Docker for development or deployment to get the best experience.

# 1. Clone the repository
git clone https://github.com/dexmal/dexbotic.git

# 2. Start Docker container
docker run -it --rm --gpus all --network host \
  -v $(pwd)/dexbotic:/dexbotic \
  dexmal/dexbotic \
  bash

# 3. Activate environment and install dependencies
cd /dexbotic
conda activate dexbotic
pip install -e .

System Requirements: Ubuntu 20.04/22.04, recommended GPUs: RTX 4090, A100, or H100 (8 GPUs recommended for training, 1 GPU for deployment).

# 1. Start Docker with Blackwell image
docker run -it --rm --gpus all --network host \
  -v /path/to/dexbotic:/dexbotic \
  dexmal/dexbotic:c130t28 \
  bash

# 2. Activate environment
cd /dexbotic
pip install -e .

• Testing and Evaluation
• Training with Provided Simulation Data
• Training with Your Own Data

The following shows a comparison of evaluation results between models trained with Dexbotic and original models on mainstream simulation environments. View more detailed evaluation results: Benchmark Results

We are continuously improving, with more features coming soon. If you like this project, please give us a star on GitHub . Your support is our motivation to keep moving forward!

If Dexbotic has been helpful in your research work, please consider citing our technical report:

@article{dexbotic,
  title={Dexbotic: Open-Source Vision-Language-Action Toolbox},
  author={Dexbotic Contributors},
  journal={arXiv preprint arXiv:2510.23511},
  year={2025}
}

This project is licensed under the MIT License.