Robotic Table Tennis: A Case Study into a High Speed Learning System
We present a deep-dive into a real-world robotic learning system that, in
previous work, was shown to be capable of hundreds of table tennis rallies with
a human and has the ability to precisely return the ball to desired targets.
This system puts together a highly optimized perception subsystem, a high-speed
low-latency robot controller, a simulation paradigm that can prevent damage in
the real world and also train policies for zero-shot transfer, and automated
real world environment resets that enable autonomous training and evaluation on
physical robots. We complement a complete system description, including
numerous design decisions that are typically not widely disseminated, with a
collection of studies that clarify the importance of mitigating various sources
of latency, accounting for training and deployment distribution shifts,
robustness of the perception system, sensitivity to policy hyper-parameters,
and choice of action space. A video demonstrating the components of the system
and details of experimental results can be found at
https://youtu.be/uFcnWjB42I0.
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David B. D'Ambrosio
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Jonathan Abelian
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Saminda Abeyruwan
64fab6f467096272ae5d3cfc
Michael Ahn
64fab6f467096272ae5d3cfd
Alex Bewley
admin_assigned
2023-09-08T11:43:27.318Z
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Alex Bewley
user
bewley
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Justin Boyd
64fab6f467096272ae5d3cff
Krzysztof Choromanski
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Omar Cortes
64fab6f467096272ae5d3d01
Erwin Coumans
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Tianli Ding
64fab6f467096272ae5d3d03
Wenbo Gao
64fab6f467096272ae5d3d04
Laura Graesser
64fab6f467096272ae5d3d05
Atil Iscen
64fab6f467096272ae5d3d06
Navdeep Jaitly
64fab6f467096272ae5d3d07
Deepali Jain
64fab6f467096272ae5d3d08
Juhana Kangaspunta
64fab6f467096272ae5d3d09
Satoshi Kataoka
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Gus Kouretas
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Yuheng Kuang
admin_assigned
2023-09-08T13:05:03.687Z
6312dd4f95407887cb7c9cd7
/avatars/44a0e80ca9bbd2138eecc6b2a8b8b226.svg
Yuheng Kuang
user
kuangyh
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Nevena Lazic
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Corey Lynch
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Reza Mahjourian
64fab6f467096272ae5d3d0f
Sherry Q. Moore
64fab6f467096272ae5d3d10
Thinh Nguyen
64fab6f467096272ae5d3d11
Ken Oslund
64fab6f467096272ae5d3d12
Barney J Reed
64fab6f467096272ae5d3d13
Krista Reymann
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Pannag R. Sanketi
claimed_verified
2024-08-09T07:49:15.902Z
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Pannag Sanketi
user
prsanky
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Anish Shankar
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Pierre Sermanet
64fab6f467096272ae5d3d17
Vikas Sindhwani
64fab6f467096272ae5d3d18
Avi Singh
64fab6f467096272ae5d3d19
Vincent Vanhoucke
64fab6f467096272ae5d3d1a
Grace Vesom
64fab6f467096272ae5d3d1b
Peng Xu
Robotic Table Tennis: A Case Study into a High Speed Learning System
We present a deep-dive into a real-world robotic learning system that, in
previous work, was shown to be capable of hundreds of table tennis rallies with
a human and has the ability to precisely return the ball to desired targets.
This system puts together a highly optimized perception subsystem, a high-speed
low-latency robot controller, a simulation paradigm that can prevent damage in
the real world and also train policies for zero-shot transfer, and automated
real world environment resets that enable autonomous training and evaluation on
physical robots. We complement a complete system description, including
numerous design decisions that are typically not widely disseminated, with a
collection of studies that clarify the importance of mitigating various sources
of latency, accounting for training and deployment distribution shifts,
robustness of the perception system, sensitivity to policy hyper-parameters,
and choice of action space. A video demonstrating the components of the system
and details of experimental results can be found at
https://youtu.be/uFcnWjB42I0.
A robotic learning system for table tennis integrates perception, control, simulation, and real-world reset mechanisms to achieve precise ball returns and autonomous training with minimal latency.
perception subsystem robot controller simulation paradigm zero-shot transfer autonomous training evaluation on physical robots latency distribution shifts robustness hyper-parameters action space