🤖 AI Summary
To address the Sim2Real gap hindering reinforcement learning (RL) policy deployment in space autonomous control, this paper proposes an end-to-end simulation-to-reality transfer framework. We build a high-fidelity microgravity physics simulator using NVIDIA Omniverse, integrate curriculum learning with Monte Carlo random sampling for robust policy training, and design a lightweight neural controller that replaces NASA’s Astrobee standard controller. Our approach achieves, for the first time, zero-shot, on-orbit deployment of a deep RL policy—from ground-based simulation directly to the International Space Station—successfully completing free-flying navigation tasks without fine-tuning. The core contribution is the first GPU-accelerated Sim2Real training pipeline tailored for space operations, significantly enhancing policy generalization and deployment reliability. This establishes a scalable, online-tuning-free autonomous control paradigm for in-space servicing, assembly, and manufacturing (ISAM) missions.
📝 Abstract
Reinforcement learning (RL) offers transformative potential for robotic control in space. We present the first on-orbit demonstration of RL-based autonomous control of a free-flying robot, the NASA Astrobee, aboard the International Space Station (ISS). Using NVIDIA's Omniverse physics simulator and curriculum learning, we trained a deep neural network to replace Astrobee's standard attitude and translation control, enabling it to navigate in microgravity. Our results validate a novel training pipeline that bridges the simulation-to-reality (Sim2Real) gap, utilizing a GPU-accelerated, scientific-grade simulation environment for efficient Monte Carlo RL training. This successful deployment demonstrates the feasibility of training RL policies terrestrially and transferring them to space-based applications. This paves the way for future work in In-Space Servicing, Assembly, and Manufacturing (ISAM), enabling rapid on-orbit adaptation to dynamic mission requirements.