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arXiv 2608.09127cs.ROcs.GR

面向机器人辅助洗澡任务的人类演示的高保真捕捉、重建与迁移

High Fidelity Capture, Reconstruction, and Transfer of Human Demonstrations for Robot-Assisted Bathing

  • School of Computer Science, Carnegie Mellon University(卡内基梅隆大学计算机科学学院)
  • School of Health and Rehabilitation Sciences, University of Pittsburgh(匹兹堡大学健康与康复科学学院)

机构由 AI 辅助整理,请以论文原文为准。

Arjun S. Lakshmipathy, Jonathan P. King, Ethan Zuo, Rohit Satishkumar, Hongyi Chen, Jeffrey Ichnowski, Dan Ding, Zackory Erickson, Nancy S. Pollard

AI总结:

针对机器人辅助洗澡任务中人类演示难以迁移的问题,提出以接触区域为核心的高保真处理框架,构建含多维度同步数据的数据集,实现灵巧软手完成洗澡任务,相关材料将公开以推动pHRI研究。

AI中文摘要:

尽管机器人在洗澡等高价值临床任务中存在需求,但现有系统仍缺乏与人类进行复杂、持续物理交互所需的安全性与可靠性。阻碍这类系统开发的核心挑战在于,即便配备现代运动与触觉传感设备,收集、理解并有效迁移高度动态、接触丰富的人类洗澡演示仍十分困难。我们提出一种简单却有效的高保真处理框架,将接触区域作为关键处理基元。利用该框架,我们构建了由训练有素的临床医生在人体受试者上完成的洗澡演示数据集。随后,我们使用该数据集设计并控制一款臂装灵巧软手,通过开环与闭环策略在人体模型上执行洗澡任务。我们的数据集是首个提供持续、接触丰富的人-人交互过程中高质量同步运动、形状、接触及力数据的数据集,且我们的迁移策略证明了这些数据在机器人堆栈多个层面的有效应用。所有相关材料将公开发布,以推动物理人机交互(pHRI)研究的进一步发展。

英文摘要:

Despite the demand for robots in high-value clinical tasks like bathing, contemporary systems still lack the safety and reliability required for complex, sustained physical interaction with humans. A key challenge hindering the development of such systems is that collecting, understanding, and effectively transferring highly dynamic, contact-rich human bathing demonstrations is difficult, even with modern motion and tactile sensing equipment. We present a straightforward, but effective framework for doing so with high fidelity by utilizing contact regions as a key processing primitive. We use our framework to build a dataset of bathing demonstrations performed by trained clinicians on human subjects. We then use this dataset to design and control an arm-mounted dexterous soft hand to perform bathing tasks on a mannequin using open- and closed-loop strategies. Our dataset is the first to provide high quality synchronized motion, shape, contact, and force during sustained, contact-rich human-human interaction, and our transfer strategies demonstrate effective use of these data across multiple levels of the robotics stack. All relevant materials will be publicly released to enable further advancements in physical human-robot interaction (pHRI) research.

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