微小重力環境における6自由度ドッキングのための強化学習：ISS-JEM内のInt-Ball2を用いたシミュレーション研究
Reinforcement Learning based 6-DoF Maneuvers for Microgravity Intravehicular Docking: A Simulation Study with Int-Ball2 in ISS-JEM
JAXAのInt-Ball2ロボットの6自由度ドッキングを、Isaac Sim上の高忠実度JEMモデルでPPOを用いて強化学習し、ドメインランダム化と観測ノイズ下で安定したドッキングを実現した。
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著者: Aman Arora, Matteo El-Hariry, Miguel Olivares-Mendez
分類: cs.RO
原文アブストラクト
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.
