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
this https URL

该篇论文深入研究了一个真实世界的机器人学习系统，该系统能够和人类进行数百次乒乓球对打，并能够精确地将球返回到指定目标。论文提供了完整的系统描述，包括通常不广泛传播的设计决策，并附上一系列研究，澄清了减轻潜在延迟来源的重要性，考虑训练和部署分布变化的影响，感知系统的稳健性，策略超参数的敏感性以及行动空间的选择。