- David B. D'Ambrosio
- Jon Abelian
- Saminda Abeyruwan
- Michael Ahn
- Alex Bewley
- Justin Boyd
- Krzysztof Choromanski
- Erwin Johan Coumans
- Tianli Ding
- Omar Escareno
- 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 Moore
- Thinh Nguyen
- Ken Oslund
- Barney J. Reed
- Krista Reymann
- Pannag Sanketi
- Anish Shankar
- Pierre Sermanet
- Vikas Sindhwani
- Avi Singh
- Vincent Vanhoucke
- Grace Vesom
- Peng Xu
Abstract
We present a deep-dive into a learning robotic 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 and novel 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 ablation studies that clarify the importance of mitigating various sources of latency, accounting for training and deployment distribution shifts, robustness of the perception system, and sensitivity to policy hyper-parameters and choice of action space. A video demonstrating the components of our system and details of experimental results is included in the supplementary material.
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