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

• URL: http://arxiv.org/abs/2208.01160v1
• Date: Mon, 1 Aug 2022 22:34:51 GMT
• Title: Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot
• Authors: Yandong Ji, Zhongyu Li, Yinan Sun, Xue Bin Peng, Sergey Levine, Glen Berseth, Koushil Sreenath
• Abstract summary: We address the problem of enabling quadrupedal robots to perform precise shooting skills in the real world using reinforcement learning. We propose a hierarchical framework that leverages deep reinforcement learning to train a robust motion control policy. 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.
• Score: 76.04391023228081
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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