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Strategy and Skill Learning for Physics-based Table Tennis Animation

DescriptionWe present a strategy and skill learning approach for physics-based table tennis animation. We demonstrate a hierarchical control system for diversified skill learning and a strategy learning framework for effective decision-making. Our strategy learning framework is validated through both agent-agent interaction and human-agent interaction in virtual reality.

Authors

Jiashun Wang

Carnegie Mellon University