title: Achieving Human Level Competitive Robot Table Tennis
summary: 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
paper_authors._id: 66b4223e4fa6a2f3fe0729a4
paper_authors.name: David B. D'Ambrosio
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paper_authors.name: Saminda Abeyruwan
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paper_authors.name: Laura Graesser
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paper_authors.name: Atil Iscen
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paper_authors.name: Heni Ben Amor
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paper_authors.name: Alex Bewley
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paper_authors.fullname: Alex Bewley
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paper_authors.name: Barney J. Reed
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paper_authors.name: Krista Reymann
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paper_authors.name: Leila Takayama
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paper_authors.name: Yuval Tassa
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paper_authors.name: Krzysztof Choromanski
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paper_authors.name: Erwin Coumans
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paper_authors.fullname: erwin coumans
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paper_authors.user: erwincoumans
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paper_authors.name: Deepali Jain
paper_authors._id: 66b4223e4fa6a2f3fe0729b1
paper_authors.name: Navdeep Jaitly
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paper_authors._id: 631119df88942700629f4aab
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paper_authors.fullname: Navdeep Jaitly
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paper_authors.name: Natasha Jaques
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paper_authors.name: Satoshi Kataoka
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paper_authors.name: Yuheng Kuang
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paper_authors.fullname: Yuheng Kuang
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paper_authors.name: Nevena Lazic
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paper_authors.name: Reza Mahjourian
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paper_authors.name: Sherry Moore
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paper_authors.name: Kenneth Oslund
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paper_authors.name: Anish Shankar
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paper_authors.name: Vikas Sindhwani
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paper_authors.name: Vincent Vanhoucke
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paper_authors.name: Grace Vesom
paper_authors._id: 66b4223e4fa6a2f3fe0729bd
paper_authors.name: Peng Xu
paper_authors._id: 66b4223e4fa6a2f3fe0729be
paper_authors.name: Pannag R. Sanketi
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paper_authors.fullname: Pannag Sanketi
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paper_title: Achieving Human Level Competitive Robot Table Tennis
paper_summary: 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
paper_ai_keywords: hierarchical and modular policy architecture low level controllers skill descriptors high level controller zero-shot sim-to-real iterative approach task distribution automatic curriculum real time adaptation