Robotic Table Tennis: A Case Study into a High Speed Learning System
Authors: name: David B. D'Ambrosio; name: Jonathan Abelian; name: Saminda Abeyruwan; name: Michael Ahn; name: Alex Bewley; name: Justin Boyd; name: Krzysztof Choromanski; name: Omar Cortes; name: Erwin Coumans; name: Tianli Ding; name: Wenbo Gao; name: Laura Graesser; name: Atil Iscen; name: Navdeep Jaitly; name: Deepali Jain; name: Juhana Kangaspunta; name: Satoshi Kataoka; name: Gus Kouretas; name: Yuheng Kuang; name: Nevena Lazic; name: Corey Lynch; name: Reza Mahjourian; name: Sherry Q. Moore; name: Thinh Nguyen; name: Ken Oslund; name: Barney J Reed; name: Krista Reymann; name: Pannag R. Sanketi; name: Anish Shankar; name: Pierre Sermanet; name: Vikas Sindhwani; name: Avi Singh; name: Vincent Vanhoucke; name: Grace Vesom; name: Peng Xu
Published: 2023-09-06 18:56:20

Abstract: 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.