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

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.