  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.
