  Recent years have seen the emergence of non-cooperative objects in space,
like failed satellites and space junk. These objects are usually operated or
collected by free-float dual-arm space manipulators. Thanks to eliminating the
difficulties of modeling and manual parameter-tuning, reinforcement learning
(RL) methods have shown a more promising sign in the trajectory planning of
space manipulators. Although previous studies demonstrate their effectiveness,
they cannot be applied in tracking dynamic targets with unknown rotation
(non-cooperative objects). In this paper, we proposed a learning system for
motion planning of free-float dual-arm space manipulator (FFDASM) towards
non-cooperative objects. Specifically, our method consists of two modules.
Module I realizes the multi-target trajectory planning for two end-effectors
within a large target space. Next, Module II takes as input the point clouds of
the non-cooperative object to estimate the motional property, and then can
predict the position of target points on an non-cooperative object. We
leveraged the combination of Module I and Module II to track target points on a
spinning object with unknown regularity successfully. Furthermore, the
experiments also demonstrate the scalability and generalization of our learning
system.
