Skip to content
aiqbench takeAI Products

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.
Sources coveringarXiv, AI Alignment Forum
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

mjorbit: A Simulation Framework for Space Robotics

arXiv2 min read

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)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Full text under the publisher's open licence · 210 words

Related coverage