实现人类水平竞争力的机器人乒乓球
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
在现实世界任务中实现人类水平的速度和表现是机器人研究社区的一个目标。这项工作迈出了实现这一目标的一步，并展示了第一个在竞技乒乓球比赛中达到业余人类水平表现的学习机器人代理。乒乓球是一项需要人类运动员经过多年训练才能达到高级熟练水平的体育运动。在本文中，我们贡献了：(1) 一个包括(i) 低层控制器及其详细技能描述符的分层模块化策略架构，用于建模代理的能力并有助于弥合模拟到真实世界的差距，以及(ii) 选择低层技能的高层控制器；(2) 实现零样本模拟到真实的技术，包括迭代方法来定义基于真实世界的任务分布并定义自动课程；以及(3) 对未知对手进行实时调整。通过29场机器人对人类的比赛评估了策略表现，其中机器人赢得了45%的比赛（13/29）。所有人类选手都是未知的，并且他们的技能水平从初学者到比赛水平不等。虽然机器人在与最高级别选手的比赛中全部失败，但在与初学者的比赛中赢得了100%的比赛，在与中级选手的比赛中赢得了55%的比赛，展示了扎实的业余人类水平表现。比赛视频可在以下网址观看：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