Chrono-Gymnasium: An Open-Source, Gymnasium-Compatible Distributed Simulation Framework
For researchers in robotics and mechanical systems, it provides a scalable way to use high-fidelity physics simulations in data-intensive tasks like RL and optimization.
Chrono-Gymnasium is a distributed simulation framework that scales high-fidelity multi-body dynamics across computing clusters, reducing wall-clock time for simulations without sacrificing accuracy. It enables RL training and Bayesian optimization for complex robotic systems.
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.