EmbodiChain

EmbodiChain#

teaser

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EmbodiChain is an end-to-end, GPU-accelerated framework for Embodied AI. It streamlines research and development by unifying high-performance simulation, real-to-sim data pipelines, modular model architectures, and efficient training workflows. This integration enables rapid experimentation, seamless deployment of intelligent agents, and effective Sim2Real transfer for real-world robotic systems.

Note

EmbodiChain is in Alpha and under active development:

  • More features will be continually added in the coming months. You can find more details in the roadmap.

  • Since this is an early release, we welcome feedback (bug reports, feature requests, etc.) via GitHub Issues.

Key Features#

  • 🚀 High-Fidelity GPU Simulation: Realistic physics for rigid & deformable objects, advanced ray-traced sensors, all GPU-accelerated for high-throughput batch simulation.

  • 🤖 Unified Robot Learning Environment: Standardized interfaces for Imitation Learning, Reinforcement Learning, and more.

  • 📊 Scalable Data Pipeline: Automated data collection, efficient processing, and large-scale generation for model training.

  • ⚡ Efficient Training & Evaluation: Online data streaming, parallel environment rollouts, and modern training paradigms.

  • 🧩 Modular & Extensible: Easily integrate new robots, environments, and learning algorithms.

The figure below illustrates the overall architecture of EmbodiChain:

frameworks

Getting Started#

To get started with EmbodiChain, follow these steps:

Citation#

If you find EmbodiChain helpful for your research, please consider citing our work:

@misc{EmbodiChain,
  author = {EmbodiChain Developers},
  title = {EmbodiChain: An end-to-end, GPU-accelerated, and modular platform for building generalized Embodied Intelligence.},
  month = {November},
  year = {2025},
  url = {https://github.com/DexForce/EmbodiChain}
}

@misc{GS-World,
   author = {Guiliang Liu and Yueci Deng and Zhen Liu and Kui Jia},
   title = {GS-World: An Efficient, Engine-driven Learning Paradigm for Pursuing Embodied Intelligence using World
      Models of Generative Simulation},
   month = {October},
   year = {2025},
   journal = {TechRxiv}
}

@inproceedings{Sim2RealVLA,
   title = {Sim2Real {VLA}: Zero-Shot Generalization of Synthesized Skills to Realistic Manipulation},
   author = {Runyi Zhao, Sheng Xu, Ruixing Jin, Yueci Deng, Yunxin Tai, Kui Jia, Guiliang Liu},
   booktitle = {The Fourteenth International Conference on Learning Representations, ICLR},
   year = {2026},
   url = {https://openreview.net/forum?id=H4SyKHjd4c}
}
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