陳曉莉，DeepMind的桌球機器人已達業餘的人類選手水準，iThome，2024-08-09
DeepMind研究人員對此一論文的主要貢獻有四，一是分層與模組化的策略架構，以低階控制器來負責機器人的具體技能，並由高階控制器來選擇與調度低階控制器；二是把自模擬環境中所學習的內容直接應用到現實的技術；三是可即時適應陌生對手的能力；最後則是在實際的賽事中對抗陌生對手的用戶研究。
David B. D'Ambrosio et al., Achieving Human Level Competitive Robot Table Tennis, arXiv:2408.03906 (Blog)
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.
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