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Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot

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  • Additional Information
    • Publication Information:
      IEEE, 2022.
    • Publication Date:
      2022
    • Abstract:
      We address the problem of enabling quadrupedal robots to perform precise shooting skills in the real world using reinforcement learning. Developing algorithms to enable a legged robot to shoot a soccer ball to a given target is a challenging problem that combines robot motion control and planning into one task. To solve this problem, we need to consider the dynamics limitation and motion stability during the control of a dynamic legged robot. Moreover, we need to consider motion planning to shoot the hard-to-model deformable ball rolling on the ground with uncertain friction to a desired location. In this paper, we propose a hierarchical framework that leverages deep reinforcement learning to train (a) a robust motion control policy that can track arbitrary motions and (b) a planning policy to decide the desired kicking motion to shoot a soccer ball to a target. We deploy the proposed framework on an A1 quadrupedal robot and enable it to accurately shoot the ball to random targets in the real world.
      Comment: Accepted to 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)
    • Accession Number:
      10.1109/iros47612.2022.9981984
    • Rights:
      OPEN
    • Accession Number:
      edsair.doi.dedup.....7cf5e50f5b8ba1cd33153e06e6fd84c9