Proceedings

Deep Reinforcement Learning for Multi-contact Motion Planning of Hexapod Robots

Huiqiao Fu, Kaiqiang Tang, Peng Li, Wenqi Zhang, Xinpeng Wang, Guizhou Deng, Tao Wang, Chunlin Chen

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 2381-2388.

https://doi.org/10.24963/ijcai.2021/328

Keywords

  • Machine Learning: Deep Reinforcement Learning
  • Robotics: Motion and Path Planning
  • Robotics: Learning in Robotics

Abstract

Legged locomotion in a complex environment requires careful planning of the footholds of legged robots. In this paper, a novel Deep Reinforcement Learning (DRL) method is proposed to implement multi-contact motion planning for hexapod robots moving on uneven plum-blossom piles. First, the motion of hexapod robots is formulated as a Markov Decision Process (MDP) with a specified reward function. Second, a transition feasibility model is proposed for hexapod robots, which describes the feasibility of the state transition under the condition of satisfying kinematics and dynamics, and in turn determines the rewards. Third, the footholds and Center-of-Mass (CoM) sequences are sampled from a diagonal Gaussian distribution and the sequences are optimized through learning the optimal policies using the designed DRL algorithm. Both of the simulation and experimental results on physical systems demonstrate the feasibility and efficiency of the proposed method. Videos are shown at https://videoviewpage.wixsite.com/mcrl.

Huiqiao Fu, Kaiqiang Tang, Peng Li, Wenqi Zhang, Xinpeng Wang, Guizhou Deng, Tao Wang, and Chunlin Chen. 2021. Deep Reinforcement Learning for Multi-contact Motion Planning of Hexapod Robots. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 2381–2388. https://doi.org/10.24963/ijcai.2021/328
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