Chrono-Gymnasium: An Open-Source, Gymnasium-Compatible Distributed Simulation Framework
Bocheng Zou, Harry Zhang, Khailanii Slaton, Jingquan Wang, Derrick Ruan, Huzaifa Mustafa Unjhawala, Radu Serban, Dan Negrut

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…
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