Quadruped robot obstacle negotiation via reinforcement learning

Authors:
  • HLHonglak Lee
    Honglak Lee
  • YSYirong Shen
    Yirong Shen
  • CYChih-Han Yu
    Chih-Han Yu
  • GSG. Singh
    G. Singh
  • ANA.Y. Ng
    A.Y. Ng
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Abstract
Legged robots can, in principle, traverse a large variety of obstacles and terrains. In this paper, we describe a successful application of reinforcement learning to the problem of negotiating obstacles with a quadruped robot. Our algorithm is based on a two-level hierarchical decomposition of the task, in which the high-level controller selects the sequence of foot-placement positions, and the low-level controller generates the continuous motions to move each foot to the specified positions. The high-level controller uses an estimate of the value function to guide its search; this estimate is learned partially from supervised data. The low-level controller is obtained via policy search. We demonstrate that our robot can successfully climb over a variety of obstacles which were not seen at training time