Monte-Carlo Planning for Agile Legged Locomotion


  • Patrick Clary Oregon State University
  • Pedro Morais Oregon State University
  • Alan Fern Oregon State University
  • Jonathan Hurst Oregon State University



robotics, planning


Recent progress in legged locomotion research has produced robots that can perform agile blind-walking with robustness comparable to a blindfolded human. However, this walking approach has not yet been integrated with planners for high-level activities. In this paper, we take a step towards high-level task planning for these robots by studying a planar simulated biped that captures their essential dynamics. We investigate variants of Monte-Carlo Tree Search (MCTS) for selecting an appropriate blind-walking controller at each decision cycle. In particular, we consider UCT with an intelligently selected rollout policy, which is shown to be capable of guiding the biped through treacherous terrain. In addition, we develop a new MCTS variant, called Monte-Carlo Discrepancy Search (MCDS), which is shown to make more effective use of limited planning time than UCT for this domain. We demonstrate the effectiveness of these planners in both deterministic and stochastic environments across a range of algorithm parameters. In addition, we present results for using these planners to control a full-order 3D simulation of Cassie, an agile bipedal robot, through complex terrain.




How to Cite

Clary, P., Morais, P., Fern, A., & Hurst, J. (2018). Monte-Carlo Planning for Agile Legged Locomotion. Proceedings of the International Conference on Automated Planning and Scheduling, 28(1), 446-450.