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