发表机构
King’s College London（伦敦国王学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对机器人抚发时毛发易漂移导致接触难调节的问题，提出TacHair框架，通过空间触觉接触分布引导在线校正，在525次试验中提升了抚发成功率与接触维持率。
AI 中文摘要
抚发是日常 grooming（护理）和个人护理中的常见动作，也广泛用于护发产品评估，因此需要具备类似物理交互能力的机器人。现有机器人护发及表面跟随方法主要依赖轨迹规划、柔顺控制、力调节或触觉条件策略，但可变形的毛发在接触时会逐渐在末端执行器上漂移，导致局部交互难以调节。我们提出TacHair，一种触觉接触分布引导的在线校正框架，将高分辨率触觉观测表示为空间毛发接触分布。视觉-触觉模仿策略生成名义抚发运动，而单独训练的残差模块校正局部接触偏差，将任务推进与接触恢复分离。我们在5种头部几何形状、3种毛发条件下开展525次真实机器人试验进行评估，成功抚发需要同时满足足够的任务推进和接触维持；与未加校正的相同视觉-触觉策略相比，本方法将成功率从42.9%提升至62.3%，接触维持率从59.4%提升至88.0%。结果表明，空间触觉接触分布是与可变形、视觉遮挡表面进行接触保持交互的有效反馈表示。演示、代码和数据集可在该https URL获取。
英文摘要
Hair stroking is common in daily grooming and personal care, and is also widely used in hair-product evaluation, motivating robots with similar physical interaction capabilities. Existing robotic hair-care and surface-following methods mainly rely on trajectory planning, compliance, force regulation, or tactile-conditioned policies, but deformable hair can remain in contact while gradually drifting across the end-effector, making local interaction difficult to regulate. We propose TacHair, a tactile contact-distribution guided online correction framework that represents high-resolution tactile observations as a spatial hair-contact distribution. A visuotactile imitation policy generates the nominal stroking motion, while a separately trained residual module corrects local contact deviations, separating task progression from contact recovery. We evaluate TacHair in 525 real-robot trials across five head geometries and three hair conditions. A successful stroke requires both sufficient task progression and contact maintenance; our method improves success from 42.9% to 62.3% and contact maintenance from 59.4% to 88.0% over the same visuotactile policy without correction. These results demonstrate spatial tactile contact distributions as an effective feedback representation for contact-preserving interaction with deformable and visually occluded surfaces. Demos, code, and datasets are available at https://tachair.github.io.
