让机器人自动撕胶带贴纱布，提升慢性伤口护理效率
Towards Autonomous Tape Handling for Robotic Wound Redressing
- 用人类操作数据训练力反馈模仿学习模型，实现精准撕胶带
- 通过数值优化轨迹确保不同身体部位贴胶无皱褶，成功率超90%
- 为智能医疗机器人提供关键胶带操作能力，适合临床自动化研发
慢性伤口（如糖尿病、压疮和静脉性溃疡）在美国影响超过650万患者，年治疗成本逾250亿美元。尽管负担沉重，伤口护理仍依赖人工，因安全要求高而难以自动化。本文提出一种自主胶带操作框架，解决伤口包扎中最基础也最困难的两个环节：胶带初始剥离（TID）与牢固贴合。针对复杂粘附动态，采用力反馈模仿学习方法，基于人类远程操作示范进行训练；针对贴合问题，开发基于数值优化的轨迹规划方法，确保在多种解剖表面实现平滑、无皱褶贴附。通过大量实验验证，方法在定量评估与完整包扎流程中均表现可靠。结果表明，胶带操作是实现实用化机器人伤口护理的关键一步。
原文摘要 · Abstract (English)
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.
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