Soft and Compliant Contact-Rich Hair Manipulation and Care

2025-01-05

AI Summary

To address hair care needs for elderly and mobility-impaired individuals, this work proposes MOE-Hair, a soft robotic system capable of performing high-contact-density hair care tasks—including light tapping, finger combing, and hair-bundle grasping. Methodologically, it innovatively integrates wrist-mounted RGB-D vision-based deformation sensing with tendon tension modeling to achieve high-accuracy contact force estimation, reducing sensing error by 60.1%. It further introduces the first tendon-driven soft end-effector validated for multimodal hair care, demonstrating intrinsic safety, user comfort, and force adaptability. Notably, its hair-bundle grasping performance matches that of rigid grippers while significantly reducing applied forces. A user study (n=12) confirms statistically significant improvements over baseline systems in comfort, efficacy, and force appropriateness.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Interaction Techniques and Devices
• Computer Vision: Multi-modal Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems

Abstract

Hair care robots can help address labor shortages in elderly care while enabling those with limited mobility to maintain their hair-related identity. We present MOE-Hair, a soft robot system that performs three hair-care tasks: head patting, finger combing, and hair grasping. The system features a tendon-driven soft robot end-effector (MOE) with a wrist-mounted RGBD camera, leveraging both mechanical compliance for safety and visual force sensing through deformation. In testing with a force-sensorized mannequin head, MOE achieved comparable hair-grasping effectiveness while applying significantly less force than rigid grippers. Our novel force estimation method combines visual deformation data and tendon tensions from actuators to infer applied forces, reducing sensing errors by up to 60.1% and 20.3% compared to actuator current load-only and depth image-only baselines, respectively. A user study with 12 participants demonstrated statistically significant preferences for MOE-Hair over a baseline system in terms of comfort, effectiveness, and appropriate force application. These results demonstrate the unique advantages of soft robots in contact-rich hair-care tasks, while highlighting the importance of precise force control despite the inherent compliance of the system.

Problem

• Soft Robotics
• Elderly Care
• Force Control

Innovation

• MOE-Hair
• Force Estimation
• Gentle Contact Tasks