SmallSatSim: A GPU-Accelerated Microgravity Robotics Toolkit for Planning, Control, and Policy Learning

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. Developing autonomy for these operations requires integrating dynamics simulation with task definition, control and learning algorithms, robustness testing, and evaluation. We present \texttt{SmallSatSim}, an open-source toolkit for developing autonomy algorithms for robots operating in microgravity environments. Built on MuJoCo, \texttt{SmallSatSim} provides a common experiment and task abstraction for spacecraft models, planners, controllers and policies, actuator effects, disturbances, and evaluation, allowing model-based and learning-based approaches to operate on the same problem definitions. The framework combines conventional MuJoCo execution for model-based control with vectorized JAX/MJX execution for massively parallel policy learning. We demonstrate \texttt{SmallSatSim} through Monte Carlo experiments on model-based control under actuator perturbations, five-seed PPO and SAC training under nominal and randomized dynamics with out-of-distribution evaluation, contact-rich rendezvous and docking, and GPU scaling experiments.