让机器人从单目视频预判对手击球，提升乒乓球回球率
LATTE-MV: Learning to Anticipate Table Tennis Hits from Monocular Videos
- 基于单目视频重建3D击球场景，实现可扩展的运动分析
- 引入不确定性感知控制器，使机器人回球率提升至59.0%
- 适合对动作预测与智能体决策感兴趣的开发者
在高速动态的乒乓球比赛中，顶尖选手不仅依赖身体敏捷性，更擅长预判对手意图以争取反应时间。本文提出一种可扩展的单目视频3D重建系统，并设计了一种考虑不确定性的预判控制器。实验表明，在仿真环境中，该策略将面对高速击球的回球率从49.9%提升至59.0%，显著优于非预判基线策略。研究为构建具备前瞻能力的智能乒乓球机器人提供了有效路径。
原文摘要 · Abstract (English)
Physical agility is a necessary skill in competitive table tennis, but by no means sufficient. Champions excel in this fast-paced and highly dynamic environment by anticipating their opponent's intent - buying themselves the necessary time to react. In this work, we take one step towards designing such an anticipatory agent. Previous works have developed systems capable of real-time table tennis gameplay, though they often do not leverage anticipation. Among the works that forecast opponent actions, their approaches are limited by dataset size and variety. Our paper contributes (1) a scalable system for reconstructing monocular video of table tennis matches in 3D and (2) an uncertainty-aware controller that anticipates opponent actions. We demonstrate in simulation that our policy improves the ball return rate against high-speed hits from 49.9% to 59.0% as compared to a baseline non-anticipatory policy.
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