arxiv preprint - Achieving Human Level Competitive Robot Table Tennis

In this episode, we discuss Achieving Human Level Competitive Robot Table Tennis by David B. D'Ambrosio, Saminda Abeyruwan, Laura Graesser, Atil Iscen, Heni Ben Amor, Alex Bewley, Barney J. Reed, Krista Reymann, Leila Takayama, Yuval Tassa, Krzysztof Choromanski, Erwin Coumans, Deepali Jain, Navdeep Jaitly, Natasha Jaques, Satoshi Kataoka, Yuheng Kuang, Nevena Lazic, Reza Mahjourian, Sherry Moore, Kenneth Oslund, Anish Shankar, Vikas Sindhwani, Vincent Vanhoucke, Grace Vesom, Peng Xu, Pannag R. Sanketi. The paper presents a learned robot agent that achieves amateur human-level performance in competitive table tennis by employing a hierarchical and modular policy architecture, including both low-level skill controllers and a high-level decision-making controller. It details techniques for zero-shot sim-to-real transfer and real-time adaptation to new opponents, achieving a 45% win rate in matches against human players of varying skill levels. While the robot consistently won against beginners and intermediates, it lost all matches against advanced players, confirming its amateur performance level.