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Embodied_AI_Simulation, developed based on MetaX C-Series GPUs, is a comprehensive simulation solution and toolkit tailored for embodied intelligence research and applications.It is designed to provide a feature-rich, technologically advanced, and high-performance simulation infrastructure that enables developers, researchers, and enterprise users to efficiently build, train, and evaluate embodied agents.

The platform seamlessly integrates leading simulation engines such as MuJoCo, cutting-edge deep reinforcement learning algorithms, standardized task benchmarks, and high-efficiency parallel training tools.Leveraging extensive industry migration practices, it offers proven methodologies to enhance the development and validation efficiency of embodied intelligence algorithms, facilitating the transition of intelligent agent technologies from research prototypes to real-world deployment.

• Frank Panda Robotic Arm In kitchen scenarios, by harnessing the general understanding and reasoning capabilities of large language models (LLMs), the robotic arm is endowed with an intelligent decision-making core.This advancement transforms conventional manually designed control workflows into a fully generative simulation process, driven by autonomous reasoning, inspiration, and decision-making.
  

• Go1 Quadruped Robot By decomposing the backflip skill of the quadruped robot into simple and independent action units, we enable the efficient transfer and training of complex skills within the MuJoCo environment.This approach not only improves training efficiency and transfer stability, but also establishes a practical and effective paradigm for developing advanced skills on the MuJoCo platform, as well as for migrating skills from environments such as Isaac Gym to MuJoCo.
  

• MetaxDRL a lightweight framework designed specifically for Deep Reinforcement Learning (DRL), offering high-quality, single-file implementations of various mainstream reinforcement learning algorithms. Its core design philosophy centers on simplicity, readability, and practicality, enabling users to quickly grasp algorithm principles while supporting flexible customization and experimental innovation.
  

This project is released under the Apache License Version 2.0. Contributions and usage are warmly welcomed.