Mobile Manipulator For Robotic Lacrosse: Learning to Pass the Ball

Abstract

This paper introduces the lacrosse mobile manipulator, a robotic system designed to play lacrosse. We focus on the task of ball passing between two robots, where challenges exist, including managing a small ball in the soft lacrosse head and interacting with a fast-moving ball. In this study, we develop innovative neural-network based learning approaches to enhance performance in dynamic environments. The robots are autonomously controlled in a decentralized manner. A combination of analytical physical-based and machine learning methods is employed to refine motion planner and ball landing predictions in both the throwing and catching processes. The system achieves satisfactory performance in real-world ball-passing experiments.