Learning coordinated badminton skills for legged manipulators
Journal:
arXiv
Published Date:
May 29, 2025
Abstract
Coordinating the motion between lower and upper limbs and aligning limb
control with perception are substantial challenges in robotics, particularly in
dynamic environments. To this end, we introduce an approach for enabling legged
mobile manipulators to play badminton, a task that requires precise
coordination of perception, locomotion, and arm swinging. We propose a unified
reinforcement learning-based control policy for whole-body visuomotor skills
involving all degrees of freedom to achieve effective shuttlecock tracking and
striking. This policy is informed by a perception noise model that utilizes
real-world camera data, allowing for consistent perception error levels between
simulation and deployment and encouraging learned active perception behaviors.
Our method includes a shuttlecock prediction model, constrained reinforcement
learning for robust motion control, and integrated system identification
techniques to enhance deployment readiness. Extensive experimental results in a
variety of environments validate the robot's capability to predict shuttlecock
trajectories, navigate the service area effectively, and execute precise
strikes against human players, demonstrating the feasibility of using legged
mobile manipulators in complex and dynamic sports scenarios.
