Reinforcement Learning based 6-DoF Maneuvers for Microgravity Intravehicular Docking: A Simulation Study with Int-Ball2 in ISS-JEM
On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 3 of its 13 references that are in the map. First posted on arXiv 15 Dec 2025.
Builds on
3 of 13 references.
Closest ancestors · 1
Same direction, newest first
- 2025RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation20 May
Other references · 2
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2018On the Continuity of Rotation Representations in Neural Networkscited by 1,214
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025Crossing the Sim2Real Gap Between Simulation and Ground Testing to Space Deployment of Autonomous Free-flyer Control
- 2025MPC-based Deep Reinforcement Learning Method for Space Robotic Control with Fuel Sloshing Mitigation
- 2026Imitation learning-based spacecraft rendezvous and docking method with Expert Demonstration
- 2024Reinforcement Learning for Docking Maneuvers with Prescribed Performance
- 2026Path Planning and Reinforcement Learning-Driven Control of On-Orbit Free-Flying Multi-Arm Robots
- 2026From Orbit Matching to Docking: A Hierarchical Reinforcement-Learning Approach to Multiphase Rendezvous Trajectory Design
- 2025Learning to Dock: A Simulation-based Study on Closing the Sim2Real Gap in Autonomous Underwater Docking
- 2024Neural-based Control for CubeSat Docking Maneuvers