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
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
66b4223e4fa6a2f3fe0729a4
David B. D'Ambrosio
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Saminda Abeyruwan
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2024-08-08T10:32:14.066Z
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Saminda Abeyruwan
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saminda
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Laura Graesser
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Atil Iscen
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Heni Ben Amor
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Alex Bewley
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2024-08-08T10:32:31.071Z
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Alex Bewley
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bewley
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Barney J. Reed
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Krista Reymann
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Leila Takayama
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Yuval Tassa
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Krzysztof Choromanski
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Erwin Coumans
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2024-08-08T10:32:54.998Z
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erwin coumans
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erwincoumans
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Deepali Jain
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Navdeep Jaitly
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2024-08-08T10:33:56.128Z
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Navdeep Jaitly
user
ndjaitly
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Natasha Jaques
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Satoshi Kataoka
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Yuheng Kuang
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2024-08-08T10:34:10.189Z
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Yuheng Kuang
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kuangyh
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Nevena Lazic
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Reza Mahjourian
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Sherry Moore
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Kenneth Oslund
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Anish Shankar
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Vikas Sindhwani
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Vincent Vanhoucke
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Grace Vesom
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Peng Xu
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Pannag R. Sanketi
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2024-08-09T07:48:19.942Z
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Pannag Sanketi
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prsanky
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
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
A robot agent achieves amateur human-level performance in competitive table tennis through a hierarchical policy architecture and real-time adaptation to unseen opponents.
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