SmallSatSim: A High-Fidelity Simulation and Training Toolkit for Microgravity Robotic Close Proximity Operations

2026-03-15

AI Summary

This work addresses the challenges of robotic proximity operations (RPO) in microgravity environments, which include free-floating control, fault tolerance, and high-fidelity dynamics modeling—tasks inadequately supported by existing simulation platforms. To bridge this gap, the authors present the first high-fidelity simulation toolkit built upon MuJoCo and MJX/JAX, uniquely leveraging MJX’s GPU-accelerated parallelism for microgravity RPO simulation. The framework integrates configurable fault injection, high-fidelity disturbance modeling, and end-to-end training capabilities. By significantly enhancing algorithmic robustness and development efficiency, this open-source, extensible platform establishes a foundational research infrastructure for autonomous, agile small-satellite operations in microgravity.

Technology Category

• Intelligent Robots: Manipulation
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Microgravity rendezvous and close proximity operations (RPO) is a growing area of interest for applications spanning in-space assembly and manufacturing (ISAM), orbital debris remediation, and small body exploration. Microgravity environments present unique challenges for robotic control and planning algorithms for new agile RPO mission scenarios like free-floating manipulation, planning under failure, and estimating high-fidelity dynamics of tumbling bodies. To facilitate the development and testing of novel RPO algorithms, we introduce SmallSatSim, a high-fidelity simulation toolkit that leverages the MuJoCo physics engine to accurately model small satellite RPO dynamics in local microgravity robotic free-flight settings, including under model disturbances and perturbations. The framework includes cutting edge out-of-the-box free-flyer control techniques. A GPU-accelerated pipeline using MuJoCo MJX and JAX is implemented for sampling- and learning-based simulation uses cases. SmallSatSim also supports configurable failure models, enabling the evaluation of safe control strategies under adversarial conditions. Visualization, logging, and GPU-enabled parallelization further enhance SmallSatSim's capability for RPO testing. We outline SmallSatSim's features and intended use cases, and demonstrate its use for robotic RPO planning and control. The open-sourced toolkit aims to accelerate research in autonomous, agile robotic small satellite operations.

Problem

• microgravity
• rendezvous and proximity operations
• robotic control
• high-fidelity simulation
• small satellite

Innovation

• SmallSatSim
• microgravity RPO
• high-fidelity simulation
• GPU-accelerated
• failure-aware control