让人形机器人实现1秒内反应的乒乓球对打
HITTER: A HumanoId Table TEnnis Robot via Hierarchical Planning and Learning
- 分层架构：先预测球路定击球点，再用强化学习控制全身动作
- 实测可连续对打106次，与另一人形机器人稳定对攻
- 融合人类动作数据，使机器人击球更自然流畅
人形机器人在行走和全身控制方面已取得显著进展，但在需要快速与动态环境交互的操控任务中仍受限。乒乓球正是此类挑战的典型：球速超过5米/秒，选手需在不足1秒内完成感知、预测与动作，兼具敏捷性与精准度。为此，我们提出一种分层式人形乒乓球框架，结合基于模型的规划器进行球轨迹预测与球拍目标规划，以及基于强化学习的全身控制器。规划器确定击球位置、速度与时机，控制器生成协调的臂腿动作，模仿人类击球并保持连续回合中的稳定与敏捷。为促进自然动作，训练中引入人类运动参考。我们在通用型人形机器人上验证系统，成功实现与人类对手连续106次击球，并能持续与另一人形机器人对打。结果表明，该系统实现了真实世界中亚秒级响应的机器人乒乓球对打，推动了敏捷、交互式人形行为的发展。
原文摘要 · Abstract (English)
Humanoid robots have recently achieved impressive progress in locomotion and whole-body control, yet they remain constrained in tasks that demand rapid interaction with dynamic environments through manipulation. Table tennis exemplifies such a challenge: with ball speeds exceeding 5 m/s, players must perceive, predict, and act within sub-second reaction times, requiring both agility and precision. To address this, we present a hierarchical framework for humanoid table tennis that integrates a model-based planner for ball trajectory prediction and racket target planning with a reinforcement learning-based whole-body controller. The planner determines striking position, velocity and timing, while the controller generates coordinated arm and leg motions that mimic human strikes and maintain stability and agility across consecutive rallies. Moreover, to encourage natural movements, human motion references are incorporated during training. We validate our system on a general-purpose humanoid robot, achieving up to 106 consecutive shots with a human opponent and sustained exchanges against another humanoid. These results demonstrate real-world humanoid table tennis with sub-second reactive control, marking a step toward agile and interactive humanoid behaviors.
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