首次在太空实现强化学习自主控制机器人，打通仿真到真实应用的鸿沟。
Crossing the Sim2Real Gap Between Simulation and Ground Testing to Space Deployment of Autonomous Free-flyer Control
- 用仿真环境训练神经网络，替代传统控制算法
- 在国际空间站成功部署，实现微重力自主导航
- 适合航天智能系统、空间机器人研发人员
强化学习（RL）为太空机器人控制带来变革性潜力。本文首次在国际空间站（ISS）上实现了基于强化学习的自由飞行机器人——NASA Astrobee的在轨自主控制。我们利用NVIDIA Omniverse物理模拟器与课程学习方法，训练深度神经网络以替代Astrobee原有的姿态与平动控制，使其能够在微重力环境下自主导航。结果验证了一种新型训练流程的有效性，该流程通过GPU加速的科学级仿真环境实现高效的蒙特卡洛强化学习训练，成功弥合了仿真到现实（Sim2Real）的差距。此次成功部署证明了在地面训练的强化学习策略可有效迁移至太空应用，为未来在轨服务、组装与制造（ISAM）奠定基础，支持快速响应动态任务需求。
原文摘要 · Abstract (English)
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.
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