机器人乒乓球：高速学习系统案例研究

摘要

我们深入研究了一个真实世界的机器人学习系统，先前的工作表明该系统能够与人类进行数百次乒乓球对打，并且能够精确地将球返回到指定目标。该系统整合了高度优化的感知子系统、高速低延迟的机器人控制器、一个可以在真实世界中防止损坏并训练零样本迁移策略的模拟范式，以及自动化的真实世界环境重置，实现了在物理机器人上的自主训练和评估。我们补充了完整的系统描述，包括通常不广泛传播的许多设计决策，以及一系列研究，阐明了减轻各种延迟来源的重要性、考虑训练和部署分布变化、感知系统的稳健性、策略超参数的敏感性以及动作空间的选择。可以在以下链接找到展示系统组件和实验结果细节的视频：https://youtu.be/uFcnWjB42I0。

English

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.

机器人乒乓球：高速学习系统案例研究

Robotic Table Tennis: A Case Study into a High Speed Learning System

摘要

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