  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 at
https://sites.google.com/view/competitive-robot-table-tennis
