aiqbench analysis
This paper introduces mjorbit, a specialized simulation framework extending MuJoCo to handle multi-body space robots with orbital dynamics and contact mechanics. By integrating efficient CPU and GPU backends, it enables realistic testing of reinforcement learning and model-predictive control for in-space servicing and manufacturing. The work directly advances robotics capabilities within the space domain, addressing a critical need for high-fidelity simulation before physical deployment.
mjorbit: A Simulation Framework for Space Robotics
arXiv:2609.08010v1 Announce Type: new Abstract: This paper presents a general framework for simulating multi-body space robots with contact. We bring efficient, large-scale robot simulation to in-space servicing, assembly, and manufacturing applications. First, we perform an empirical trade study of methods for coupling orbit propagation with existing robotics simulation frameworks. Next, we present mjorbit, a general, flexible, and performant framework built on the MuJoCo engine widely used in robotics, to which we add key spacecraft dynamics, actuators, and sensors. We provide a low-latency C++ CPU backend and a high-throughput GPU backend behind a simple Python API. We demonstrate mjorbit by solving several realistic on-orbit case studies with both model-predictive control and reinforcement learning. Open-source code and examples are available at: https://johnzhang3.github.io/mjorbit/
Abstract:This paper presents a general framework for simulating multi-body space robots with contact. We bring efficient, large-scale robot simulation to in-space servicing, assembly, and manufacturing applications. First, we perform an empirical trade study of methods for coupling orbit propagation with existing robotics simulation frameworks. Next, we present mjorbit, a general, flexible, and performant framework built on the MuJoCo engine widely used in robotics, to which we add key spacecraft dynamics, actuators, and sensors. We provide a low-latency C++ CPU backend and a high-throughput GPU backend behind a simple Python API. We demonstrate mjorbit by solving several realistic on-orbit case studies with both model-predictive control and reinforcement learning. Open-source code and examples are available at: this https URL
Robotics (cs.RO)
arXiv:2609.08010 [cs.RO]
(or arXiv:2609.08010v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.08010 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 210 words
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