AdaPT: Towards Professional Tennis Styles for Humanoid Robots with Adaptive Motion Planning and Tracking
Score
70提出将风格化规划与鲁棒跟踪解耦的框架，并针对真实场景的误差累积给出可操作的适应机制，信息增量明显。
论文结构完整，包含仿真实验、真实机器人验证和工程经验总结，论证链条扎实。
公开视频与代码，并在两款不同尺寸人形机器人上验证，为后续研究提供可复现的基线。
人形机器人与运动智能正处于爆发期，该研究直接回应了当前行业对动态运动控制与仿真迁移的现实需求。
来源为arXiv AI/CL/LG，且有项目网站、视频与代码支撑，可信度较高。
Base 78 × T2 Source weight ×0.9 = 70
Curation line 70Selected
Why selected
The paper stands out not for a massive model release but for its precise hierarchical design and adaptation mechanism that tackles the practical conflict between motion style and task performance in humanoid robots.
AI commentary
「What makes this paper worth reading is that it does not merely chase fancy motions in simulation but seriously addresses the snowballing errors that complex styles introduce on real robots. For teams working on robot control and sim2real, AdaPT's hierarchical thinking and speed-adapter design offer a directly useful reference; for those watching embodied AI products, the real-machine demos on Unitree and Dobot show this technology is closer to practical use than one might expect.」
Quick overview
Humanoid robots have shown promising capabilities in real-world ball sports, but achieving professional motion styles while maintaining strong task performance remains challenging. This paper introduces AdaPT, an Adaptive Motion Planning and Tracking framework that learns professional tennis serving and rally styles directly from broadcast videos. The hierarchical design lets a planner generate stylistic kinematic motions while a tracker executes them with minimal interference. To address the sim-to-real gap, the adaptation mechanism learns to track randomized execution speeds and conditions the planner on a learned motion-speed adapter to mitigate compounding errors. Real-world experiments on the Unitree G1 and the full-size Dobot Atom humanoid robot demonstrate the effectiveness of the approach and provide insights for future humanoid ball-sports systems.
- AdaPT uses a hierarchical design where a planner generates stylistic kinematic motions and a tracker executes them with minimal interference with planning goals.
- An adaptation mechanism trains the tracker with randomized execution speeds and conditions the planner on a learned motion-speed adapter to reduce compounding errors.
- Real-world validation on Unitree G1 and the 1.7m full-size Dobot Atom humanoid robot achieves in-the-wild serving without motion capture.
Full text
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