用分布式计算加速高精度机器人仿真，提升训练效率。
Chrono-Gymnasium: An Open-Source, Gymnasium-Compatible Distributed Simulation Framework
- 基于Ray构建分布式框架，兼容Gymnasium接口
- 仿真耗时显著降低，物理精度不下降
- 适合强化学习与优化设计等复杂系统研究
高保真物理仿真对缩小机器人和复杂机械系统中仿真到现实的差距至关重要。然而，高保真引擎的计算开销常限制其在强化学习（RL）和全局优化等数据密集型任务中的应用。我们提出Chrono-Gymnasium，一个基于Ray框架的分布式计算框架，可将Project Chrono的高保真多体动力学仿真扩展至大规模计算集群。该框架提供标准化的Gymnasium接口，便于与现代机器学习库无缝集成，并内置同步与消息传递原语以支持分布式执行。通过两个案例验证：（1）在复杂地形中训练自主机器人导航的RL智能体；（2）通过贝叶斯优化调整行星着陆器设计参数以保证着陆稳定性。结果表明，Chrono-Gymnasium在不牺牲物理精度的前提下显著缩短了高保真仿真的墙钟时间，为复杂机器人系统的设计与控制提供了可扩展路径。
原文摘要 · Abstract (English)
High-fidelity physics simulation is essential for closing the sim-to-real gap in robotics and complex mechanical systems. However, the computational overhead of high-fidelity engines often limits their use in data-intensive tasks like Reinforcement Learning (RL) and global optimization. We introduce Chrono-Gymnasium, a distributed computing framework that scales the high-fidelity multi-body dynamics of Project Chrono across large-scale computing clusters. Built upon the Ray framework, Chrono-Gymnasium provides a standardized Gymnasium interface, enabling seamless integration with modern machine learning libraries while providing built-in synchronization and messaging primitives for distributed execution. We demonstrate the framework's capabilities through two distinct case studies: (1) the training of an RL agent for autonomous robotic navigation in complex terrains, and (2) the Bayesian Optimization of a planetary lander's design parameters to ensure landing stability. Our results show that Chrono-Gymnasium reduces wall-clock time for high-fidelity simulations without sacrificing physical accuracy, offering a scalable path for the design and control of complex robotic systems.
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