让机器人精准控制与物体的接触，实现真实物理交互。
ContactMimic: Humanoid Object Interaction via Contact Control
- 同时追踪关键点和接触状态，解耦动作与接触关系。
- 在10种交互任务中实现可控接触，无需特定奖励函数。
- 支持真实世界部署，5种动作均验证接触可调控性。
仅靠关键点追踪无法完成坐椅子、擦黑板或推家具等物体交互任务，因机器人可达目标姿态却无实质接触。本文提出CONTACTMIMIC框架，同时追踪部件级二值接触指令与关键点轨迹。通过接触跟随奖励与轨迹增强策略，打破关键点轨迹与接触标签间的相关性，使策略成功解耦接触行为与几何姿态，实现精确物理接触及接触可控性（可主动产生或抑制接触）。仿真测试涵盖10类多样化的真人-物体交互动作，结果表明，该方法无需任务特定奖励即可完成操作，且在接触相关任务上优于仅用关键点的追踪器。消融实验验证了轨迹增强方案的必要性，跨域部署实验证明其在真实世界中对5种动作均具备接触可控能力。视频演示见https://lixinyao11.github.io/contactmimic-page/。
原文摘要 · Abstract (English)
Keypoint tracking alone is insufficient for object interaction tasks such as sitting on a chair, wiping a board, or pushing furniture, where the robot can reach the correct pose without making meaningful physical contact with the object. We present CONTACTMIMIC, a learning framework that tracks explicit partlevel binary contact commands alongside keypoint trajectories. CONTACTMIMIC is made possible through the use of contact-following rewards and a trajectory augmentation scheme aimed at breaking the correlations between keypoint trajectories and contact labels. The resulting policy successfully decouples contact behavior from keypoint geometry, and achieves precise physical contact as well as contact-controllability (produce or suppress contact during deployment as desired). Simulation experiments across 10 diverse human-object interaction motions confirm that CONTACTMIMIC exhibits contact controllability that enables it to complete manipulation tasks without task-specific rewards, while also outperforming keypoint-only trackers on contact-relevant tasks. Ablations confirm the necessity of the proposed trajectory augmentation scheme and sim2real deployment validates contact controllability in the real world across 5 different motions. Video results are available on https://lixinyao11.github.io/contactmimic-page/.
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