Chrono-Gymnasium: An Open-Source, Gymnasium-Compatible Distributed Simulation Framework

TL;DR

Chrono-Gymnasium is a scalable, distributed simulation framework that integrates high-fidelity physics engines with machine learning tools, enabling efficient RL training and optimization in robotics.

Contribution

It introduces a distributed, Gymnasium-compatible framework based on Project Chrono and Ray, facilitating large-scale physics simulations for robotics applications.

Findings

Reduces wall-clock time for high-fidelity simulations

Enables scalable RL training in complex terrains

Supports Bayesian optimization for robotic design

Abstract

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.