Reinforcement learning with prior policy guidance for motion planning of dual-arm free-floating space robot

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

4 Scopus Citations
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  • Yuxue Cao
  • Shengjie Wang
  • Wenke Ma
  • Xinru Xie
  • Lei Liu

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Original languageEnglish
Article number108098
Journal / PublicationAerospace Science and Technology
Online published9 Jan 2023
Publication statusPublished - May 2023


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 || • || to build a more reasonable reward function. Furthermore, our method successfully captures a rotating object with different spinning speeds. © 2023 Elsevier Masson SAS. All rights reserved.