Towards Autonomous Tape Handling for Robotic Wound Redressing
Xiao Liang, Lu Shen, Peihan Zhang, Soofiyan Atar, Florian Richter, Michael C. Yip
AI summary
Problem
Chronic wound care remains a manual, labor-intensive process that strains healthcare systems and caregivers. Automating it requires overcoming the complex adhesive dynamics of tape detachment and the geometric challenges of secure tape placement on diverse anatomical surfaces.
Approach
The framework pairs a force-feedback imitation learning policy, trained on human teleoperation data, for initiating tape detachment with a numerical trajectory optimization method that plans smooth, wrinkle-free placement paths across varying skin geometries.
Key results
- Force-feedback imitation learning reduces contact forces while successfully initiating tape detachment
- Trajectory optimization generates wrinkle-free, secure tape placement across complex anatomical surfaces
- Policies generalize to unseen human skin and varied tape roll geometries
- Integrated pipeline demonstrates multi-step autonomous wound redressing
Why it matters
Solves a critical manipulation bottleneck, paving the way for scalable, cost-effective robotic automation of home and clinical wound care.
Abstract
Chronic wounds, such as diabetic, pressure, and venous ulcers, affect over 6.5 million patients in the United States alone and generate an annual cost exceeding $25 billion. Despite this burden, chronic wound care remains a routine yet manual process performed exclusively by trained clinicians due to its critical safety demands. We envision a future in which robotics and automation support wound care to lower costs and enhance patient outcomes. This paper introduces an autonomous framework for one of the most fundamental yet challenging subtasks in wound redressing: adhesive tape manipulation. Specifically, we address two critical capabilities: tape initial detachment (TID) and secure tape placement. To handle the complex adhesive dynamics of detachment, we propose a force-feedback imitation learning approach trained from human teleoperation demonstrations. For tape placement, we develop a numerical trajectory optimization method based to ensure smooth adhesion and wrinkle-free application across diverse anatomical surfaces. We validate these methods through extensive experiments, demonstrating reliable performance in both quantitative evaluations and integrated wound redressing pipelines. Our results establish tape manipulation as an essential step toward practical robotic wound care automation.