Strategy and Skill Learning for Physics-based Table Tennis Animation

TL;DR

This paper introduces a hierarchical control and strategy learning framework for physics-based table tennis animation, enabling diverse skill execution and decision-making in dynamic environments, validated through VR interactions.

Contribution

It presents a novel hierarchical control system combined with strategy learning to improve skill diversity and decision-making in physics-based character animation.

Findings

Effective in executing diverse table tennis skills

Outperforms state-of-the-art methods in comparative analysis

Validated through VR agent-agent and human-agent interactions

Abstract

Recent advancements in physics-based character animation leverage deep learning to generate agile and natural motion, enabling characters to execute movements such as backflips, boxing, and tennis. However, reproducing the selection and use of diverse motor skills in dynamic environments to solve complex tasks, as humans do, still remains a challenge. We present a strategy and skill learning approach for physics-based table tennis animation. Our method addresses the issue of mode collapse, where the characters do not fully utilize the motor skills they need to perform to execute complex tasks. More specifically, we demonstrate a hierarchical control system for diversified skill learning and a strategy learning framework for effective decision-making. We showcase the efficacy of our method through comparative analysis with state-of-the-art methods, demonstrating its capabilities in executing various skills for table tennis. Our strategy learning framework is validated through both agent-agent interaction and human-agent interaction in Virtual Reality, handling both competitive and cooperative tasks.