機器人桌球：一個高速學習系統的案例研究

摘要

我們深入探討一個真實世界的機器人學習系統，先前的研究表明該系統能夠與人類進行數百次乒乓球對打，並具有將球精確返回到指定目標的能力。該系統結合了高度優化的感知子系統、高速低延遲的機器人控制器、一個可以在真實世界中防止損壞並訓練零樣本轉移策略的模擬範式，以及自動重置真實世界環境，實現對物理機器人的自主訓練和評估。我們補充了一個完整的系統描述，包括通常不廣泛傳播的許多設計決策，並附上一系列研究，澄清了緩解各種延遲來源、考慮訓練和部署分布變化、感知系統的穩健性、對策略超參數的敏感性以及行動空間的選擇等重要性。系統組件的演示視頻和實驗結果的詳細信息可在以下網址找到：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.

機器人桌球：一個高速學習系統的案例研究

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

摘要

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