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。

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

General-purpose robotic systems must master a large repertoire of diverse
skills to be useful in a range of daily tasks. While reinforcement learning
provides a powerful framework for acquiring individual behaviors, the time
needed to acquire each skill makes the prospect of a generalist robot trained
with RL daunting. In this paper, we study how a large-scale collective robotic
learning system can acquire a repertoire of behaviors simultaneously, sharing
exploration, experience, and representations across tasks. In this framework
new tasks can be continuously instantiated from previously learned tasks
improving overall performance and capabilities of the system. To instantiate
this system, we develop a scalable and intuitive framework for specifying new
tasks through user-provided examples of desired outcomes, devise a multi-robot
collective learning system for data collection that simultaneously collects
experience for multiple tasks, and develop a scalable and generalizable
multi-task deep reinforcement learning method, which we call MT-Opt. We
demonstrate how MT-Opt can learn a wide range of skills, including semantic
picking (i.e., picking an object from a particular category), placing into
various fixtures (e.g., placing a food item onto a plate), covering, aligning,
and rearranging. We train and evaluate our system on a set of 12 real-world
tasks with data collected from 7 robots, and demonstrate the performance of our
system both in terms of its ability to generalize to structurally similar new
tasks, and acquire distinct new tasks more quickly by leveraging past
experience. We recommend viewing the videos at
this https URL

研究一种大规模集体机器人学习系统，可以同时获取多项行为并跨越不同任务，通过 MT-Opt 来学习广泛技能，并证明其在结构相似的新任务上可以泛化并且可以通过利用过去的经验更快地获取独特的新任务，从而提高系统的性能和功能。