Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot
This addresses the challenge of combining motion control and planning for dynamic legged robots in real-world tasks, but it is incremental as it applies existing hierarchical methods to a specific domain.
The paper tackled the problem of enabling a quadrupedal robot to perform precise soccer shooting skills in the real world by proposing a hierarchical reinforcement learning framework, resulting in the robot accurately shooting a ball to random targets.
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.