用强化学习实现动作模仿，开源框架支持图形与机器人研究。
MimicKit: A Reinforcement Learning Framework for Motion Imitation and Control
- 结合动作模仿与强化学习训练运动控制器
- 提供标准化环境与模块化代码结构
- 适合动画生成、机器人控制等场景研究者使用
MimicKit 是一个用于通过动作模仿和强化学习训练运动控制器的开源框架。代码库实现了常用的动作模仿技术和强化学习算法，旨在通过统一的训练框架以及标准化的环境、智能体和数据结构，支持计算机图形学和机器人领域的研究与应用。该代码库设计为模块化且易于配置，便于对新角色和任务进行修改与扩展。开源代码已发布于：https://github.com/xbpeng/MimicKit。
原文摘要 · Abstract (English)
MimicKit is an open-source framework for training motion controllers using motion imitation and reinforcement learning. The codebase provides implementations of commonly-used motion-imitation techniques and RL algorithms. This framework is intended to support research and applications in computer graphics and robotics by providing a unified training framework, along with standardized environment, agent, and data structures. The codebase is designed to be modular and easily configurable, enabling convenient modification and extension to new characters and tasks. The open-source codebase is available at: https://github.com/xbpeng/MimicKit.
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