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Achieving Human Level Competitive Robot Table Tennis
Achieving human-level speed and performance on real world tasks is a north star for the robotics research community. This work takes a step towards that goal and presents the first learned robot agent that reaches amateur human-level performance in competitive table tennis. Table tennis is a
D'Ambrosio¹*, Saminda Abeyruwan¹*, Laura Graesser¹*, Atil Iscen¹, Heni Ben Amor², Alex Bewley², Barney J. Reed²^, Krista Reymann², Leila Takayama²+, Yuval Tassa², 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, and Pannag R. Sanketi¹ This work takes a step towards that goal and presents the first learned robot agent that reaches amateur human-level performance in competitive table tennis. Table Tennis is thus a valuable benchmark for advancing robotic capabilities, including high-speed motion, real-time precise and strategic decision making, system design and enabling direct competition with a human opponent.
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