用不完整的真人网球动作数据，教会机器人打网球。
Learning Athletic Humanoid Tennis Skills from Imperfect Human Motion Data
- 从零散的真人网球动作片段中学习基础技能
- 实机部署后能稳定与人对打多拍回合
- 适合想快速训练机器人运动能力的研究者
人类运动员在对抗性网球比赛中展现出高度动态且多样的技术，但将此类行为复现于类人机器人仍具挑战，主要因缺乏完整精确的人体动作数据。本文提出LATENT系统，能够从不完美的真人网球动作数据中学习类人机器人网球技能。这些数据仅包含网球运动中的基础动作片段，而非完整比赛序列，显著降低数据采集难度。关键洞察在于：尽管不完整，这些准真实数据仍蕴含人类网球基础技能的先验信息。通过进一步修正与组合，我们训练出可在多种条件下稳定击球并准确回球至目标位置的类人机器人策略，同时保持自然运动风格。我们还设计了鲁棒的仿真到现实迁移方案，并成功部署于Unitree G1类人机器人。实机测试表明，该方法在真实世界表现惊人，可稳定与人类玩家进行多拍对打。项目页面：https://zzk273.github.io/LATENT/
原文摘要 · Abstract (English)
Human athletes demonstrate versatile and highly-dynamic tennis skills to successfully conduct competitive rallies with a high-speed tennis ball. However, reproducing such behaviors on humanoid robots is difficult, partially due to the lack of perfect humanoid action data or human kinematic motion data in tennis scenarios as reference. In this work, we propose LATENT, a system that Learns Athletic humanoid TEnnis skills from imperfect human motioN daTa. The imperfect human motion data consist only of motion fragments that capture the primitive skills used when playing tennis rather than precise and complete human-tennis motion sequences from real-world tennis matches, thereby significantly reducing the difficulty of data collection. Our key insight is that, despite being imperfect, such quasi-realistic data still provide priors about human primitive skills in tennis scenarios. With further correction and composition, we learn a humanoid policy that can consistently strike incoming balls under a wide range of conditions and return them to target locations, while preserving natural motion styles. We also propose a series of designs for robust sim-to-real transfer and deploy our policy on the Unitree G1 humanoid robot. Our method achieves surprising results in the real world and can stably sustain multi-shot rallies with human players. Project page: https://zzk273.github.io/LATENT/
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