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 physically demanding sport which requires human players to undergo years of training to achieve an advanced level of proficiency. In this paper, we contribute (1) a hierarchical and modular policy architecture consisting of (i) low level controllers with their detailed skill descriptors which model the agent's capabilities and help to bridge the sim-to-real gap and (ii) a high level controller that chooses the low level skills, (2) techniques for enabling zero-shot sim-to-real including an iterative approach to defining the task distribution that is grounded in the real-world and defines an automatic curriculum, and (3) real time adaptation to unseen opponents. Policy performance was assessed through 29 robot vs. human matches of which the robot won 45% (13/29). All humans were unseen players and their skill level varied from beginner to tournament level. Whilst the robot lost all matches vs. the most advanced players it won 100% matches vs. beginners and 55% matches vs. intermediate players, demonstrating solidly amateur human-level performance. Videos of the matches can be viewed atthis https URL
View on arXiv@article{dámbrosio2025_2408.03906,
title={ Achieving Human Level Competitive Robot Table Tennis },
author={ David B. DÁmbrosio and Saminda Abeyruwan and Laura Graesser and Atil Iscen and Heni Ben Amor and Alex Bewley and Barney J. Reed and Krista Reymann and Leila Takayama and Yuval Tassa and Krzysztof Choromanski and Erwin Coumans and Deepali Jain and Navdeep Jaitly and Natasha Jaques and Satoshi Kataoka and Yuheng Kuang and Nevena Lazic and Reza Mahjourian and Sherry Moore and Kenneth Oslund and Anish Shankar and Vikas Sindhwani and Vincent Vanhoucke and Grace Vesom and Peng Xu and Pannag R. Sanketi },
journal={arXiv preprint arXiv:2408.03906},
year={ 2025 }
}