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

2026-05-14

AI Summary

High-fidelity physical simulation is often hindered by substantial computational costs, limiting its applicability to data-intensive tasks such as reinforcement learning and global optimization. This work proposes a Ray-based distributed simulation framework that, for the first time, integrates the high-fidelity multibody dynamics engine Project Chrono with the Gymnasium standard interface. By incorporating built-in synchronization and communication mechanisms, the framework enables efficient distributed execution without compromising physical accuracy. Empirical evaluations demonstrate significant reductions in simulation time while preserving fidelity, with scalability and practical utility validated through two challenging case studies: robot navigation over complex terrain using reinforcement learning and planetary lander design via Bayesian optimization.

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.

Problem

• high-fidelity simulation
• sim-to-real gap
• computational overhead
• data-intensive tasks
• robotics

Innovation

• distributed simulation
• high-fidelity physics
• Gymnasium-compatible
• multi-body dynamics
• scalable reinforcement learning