Reinforcement learning methods as a promising technique have achieved
superior results in the motion planning of free-floating space robots. However,
due to the increase in planning dimension and the intensification of system
dynamics coupling, the motion planning of dual-arm free-floating space robots
remains an open challenge. In particular, the current study cannot handle the
task of capturing a non-cooperative object due to the lack of the pose
constraint of the end-effectors. To address the problem, we propose a novel
algorithm, EfficientLPT, to facilitate RL-based methods to improve planning
accuracy efficiently. Our core contributions are constructing a mixed policy
with prior knowledge guidance and introducing infinite norm to build a more
reasonable reward function. Furthermore, our method successfully captures a
rotating object with different spinning speeds.

我们提出了一种新的算法 EfficientLPT，通过使用混合策略、引入先验知识，以及采用无穷范数构建合理的奖励函数来提高强化学习方法的规划精度，从而解决了双臂自由浮动空间机器人动作规划中无法捕捉非合作对象的问题，并通过旋转速度不同的物体捕获任务验证了该方法的有效性。