Reinforcement Learning based 6-DoF Maneuvers for Microgravity Intravehicular Docking: A Simulation Study with Int-Ball2 in ISS-JEM

TL;DR

This paper develops a reinforcement learning framework for 6-DoF docking of the Int-Ball2 robot inside the ISS, demonstrating stable performance under realistic microgravity conditions and sensor noise in a high-fidelity simulation.

Contribution

It introduces a domain-randomized RL approach for microgravity docking that explicitly models propulsion physics and environmental variability, advancing autonomous space robot control.

Findings

RL controllers achieved stable docking in simulation

Explicit physics modeling improved transferability

Robust performance under sensing noise and actuation mismatches

Abstract

Autonomous free-flyers play a critical role in intravehicular tasks aboard the International Space Station (ISS), where their precise docking under sensing noise, small actuation mismatches, and environmental variability remains a nontrivial challenge. This work presents a reinforcement learning (RL) framework for six-degree-of-freedom (6-DoF) docking of JAXA's Int-Ball2 robot inside a high-fidelity Isaac Sim model of the Japanese Experiment Module (JEM). Using Proximal Policy Optimization (PPO), we train and evaluate controllers under domain-randomized dynamics and bounded observation noise, while explicitly modeling propeller drag-torque effects and polarity structure. This enables a controlled study of how Int-Ball2's propulsion physics influence RL-based docking performance in constrained microgravity interiors. The learned policy achieves stable and reliable docking across varied conditions and lays the groundwork for future extensions pertaining to Int-Ball2 in collision-aware navigation, safe RL, propulsion-accurate sim-to-real transfer, and vision-based end-to-end docking.