達到人類水準競爭力的桌球機器人

Achieving Human Level Competitive Robot Table Tennis

August 7, 2024

作者: David B. D'Ambrosio, Saminda Abeyruwan, Laura Graesser, Atil Iscen, Heni Ben Amor, Alex Bewley, Barney J. Reed, Krista Reymann, Leila Takayama, Yuval Tassa, Krzysztof Choromanski, Erwin Coumans, Deepali Jain, Navdeep Jaitly, Natasha Jaques, Satoshi Kataoka, Yuheng Kuang, Nevena Lazic, Reza Mahjourian, Sherry Moore, Kenneth Oslund, Anish Shankar, Vikas Sindhwani, Vincent Vanhoucke, Grace Vesom, Peng Xu, Pannag R. Sanketi

cs.AI

摘要

在真實世界任務中達到人類水準的速度和表現是機器人研究社區的目標。這項工作邁出了邁向該目標的一步，提出了第一個在競爭性乒乓球比賽中達到業餘人類水準表現的學習機器人代理。乒乓球是一項需要人類選手經過多年訓練才能達到高級熟練水準的體育運動。在本文中，我們貢獻了(1) 一種階層式和模塊化的策略架構，包括(i) 具有詳細技能描述的低層控制器，這些描述模擬了代理的能力並有助於彌合模擬與現實之間的差距，以及(ii) 選擇低層技能的高層控制器，(2) 實現零樣本模擬到現實的技術，包括通過迭代方法定義以真實世界為基礎的任務分配並定義自動課程，以及(3) 對未知對手進行實時適應。通過29場機器人對人類的比賽評估了策略表現，其中機器人贏得了45%的比賽（13/29）。所有人類都是未曾見過的玩家，他們的技能水平從初學者到比賽級別不等。儘管機器人在與最高級別玩家的比賽中全部失利，但在與初學者的比賽中贏得了100%的比賽，在與中級玩家的比賽中贏得了55%的比賽，展示了堅實的業餘人類水準表現。比賽的視頻可在以下網址查看：https://sites.google.com/view/competitive-robot-table-tennis

English

Achieving human-level speed and performance on real world tasks is a north star for the robotics research community. This work takes a step towards that goal and presents the first learned robot agent that reaches amateur human-level performance in competitive table tennis. Table tennis is a physically demanding sport which requires human players to undergo years of training to achieve an advanced level of proficiency. In this paper, we contribute (1) a hierarchical and modular policy architecture consisting of (i) low level controllers with their detailed skill descriptors which model the agent's capabilities and help to bridge the sim-to-real gap and (ii) a high level controller that chooses the low level skills, (2) techniques for enabling zero-shot sim-to-real including an iterative approach to defining the task distribution that is grounded in the real-world and defines an automatic curriculum, and (3) real time adaptation to unseen opponents. Policy performance was assessed through 29 robot vs. human matches of which the robot won 45% (13/29). All humans were unseen players and their skill level varied from beginner to tournament level. Whilst the robot lost all matches vs. the most advanced players it won 100% matches vs. beginners and 55% matches vs. intermediate players, demonstrating solidly amateur human-level performance. Videos of the matches can be viewed at https://sites.google.com/view/competitive-robot-table-tennis