Space robots have played a critical role in autonomous maintenance and space junk removal. Multi-arm space robots can efficiently complete the target capture and base reorientation tasks due to their flexibility and the collaborative capabilities between the arms. However, the complex coupling properties arising from both the multiple arms and the free-floating base present challenges to the motion planning problems of multi-arm space robots. We observe that the octopus elegantly achieves similar goals when grabbing prey and escaping from danger. Inspired by the distributed control of octopuses' limbs, we develop a multi-level decentralized motion planning framework to manage the movement of different arms of space robots. This motion planning framework integrates naturally with the multi-agent reinforcement learning (MARL) paradigm. The results indicate that our method outperforms the previous method (centralized training). Leveraging the flexibility of the decentralized framework, we reassemble policies trained for different tasks, enabling the space robot to complete trajectory planning tasks while adjusting the base attitude without further learning. Furthermore, our experiments confirm the superior robustness of our method in the face of external disturbances, changing base masses, and even the failure of one arm.

翻译：空间机器人在自主维护和太空垃圾清理中发挥着关键作用。多臂空间机器人因其灵活性以及各臂间的协作能力，可高效完成目标捕获和基座重新定向任务。然而，多臂结构与自由漂浮基座产生的复杂耦合特性给多臂空间机器人的运动规划问题带来了挑战。我们观察到，章鱼在抓捕猎物和逃离危险时也能优雅地实现类似目标。受章鱼肢体分布式控制的启发，我们开发了一种多级分散式运动规划框架，用于管理空间机器人不同臂的运动。该运动规划框架与多智能体强化学习（MARL）范式自然契合。结果表明，我们的方法优于先前的方法（集中式训练）。利用分散式框架的灵活性，我们重新组装了针对不同任务训练的策略，使空间机器人能够在无需进一步学习的情况下完成轨迹规划任务并调整基座姿态。此外，我们的实验证实了该方法在外部干扰、基座质量变化甚至单臂故障情况下的卓越鲁棒性。