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