TacHair: Tactile Contact-Distribution Guided Online Correction for Robotic Hair Stroking and Perception

2026-10-07

AI Summary

This study addresses contact drift and interaction loss during robotic hair combing caused by hair strand deformation. To tackle this, we propose TacHair, a framework that represents high-resolution tactile observations as spatial contact distributions to guide online correction. TacHair employs a visuo-tactile imitation strategy to generate nominal actions for task progression while training an independent residual module to correct contact deviations, thereby decoupling task execution from contact recovery. Across 525 real-world experiments, the proposed method increases task success rate from 42.9% to 62.3% and contact maintenance rate from 59.4% to 88.0%. These results demonstrate that TacHair effectively enables robust contact-preserving interaction on deformable, occluded surfaces.

Abstract

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.

Problem

• robotic hair stroking
• deformable surface interaction
• tactile perception
• contact maintenance
• visually occluded surfaces

Innovation

• tactile contact-distribution
• online correction
• residual module
• visuotactile imitation policy
• deformable surface interaction