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.
@article{arxiv.2605.14911,
title = {Chrono-Gymnasium: An Open-Source, Gymnasium-Compatible Distributed Simulation Framework},
author = {Bocheng Zou and Harry Zhang and Khailanii Slaton and Jingquan Wang and Derrick Ruan and Huzaifa Mustafa Unjhawala and Radu Serban and Dan Negrut},
journal= {arXiv preprint arXiv:2605.14911},
year = {2026}
}