四足歩行ロボットによる高精度サッカーシュートのための階層型強化学習
Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot
四足歩行ロボットが実世界で正確にサッカーボールを目標地点へ蹴るため、運動制御と計画を階層的に強化学習するフレームワークを提案し、A1ロボットで実証した。
著者: Yandong Ji, Zhongyu Li, Yinan Sun, Xue Bin Peng, Sergey Levine, Glen Berseth, Koushil Sreenath
分類: cs.RO, cs.AI, cs.SY, eess.SY
原文アブストラクト
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.
