Paper Info
Title
Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot.
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.
Year
DOI
Venue
2022
10.1109/IROS47612.2022.9981984
IEEE/RJS International Conference on Intelligent RObots and Systems (IROS)
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Yandong Ji100.34
Zhongyu Li200.34
Yinan Sun3267.14
Xue Bin Peng41849.70
Sergey Levine53377182.21
Glen Berseth615215.35
Koushil Sreenath735833.41