Chrono-Gymnasium: An Open-Source, Gymnasium-Compatible Distributed Simulation Framework
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arXiv
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| Format: | Preprint |
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2026
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| author | Zou, Bocheng Zhang, Harry Slaton, Khailanii Wang, Jingquan Ruan, Derrick Unjhawala, Huzaifa Mustafa Serban, Radu Negrut, Dan |
| author_facet | Zou, Bocheng Zhang, Harry Slaton, Khailanii Wang, Jingquan Ruan, Derrick Unjhawala, Huzaifa Mustafa Serban, Radu Negrut, Dan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14911 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Chrono-Gymnasium: An Open-Source, Gymnasium-Compatible Distributed Simulation Framework Zou, Bocheng Zhang, Harry Slaton, Khailanii Wang, Jingquan Ruan, Derrick Unjhawala, Huzaifa Mustafa Serban, Radu Negrut, Dan Robotics 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. |
| title | Chrono-Gymnasium: An Open-Source, Gymnasium-Compatible Distributed Simulation Framework |
| topic | Robotics |
| url | https://arxiv.org/abs/2605.14911 |