发表机构
University of Illinois at Urbana-Champaign; Case Western Reserve University（伊利诺伊大学厄巴纳-香槟分校; 凯斯西储大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对社交物理人机交互中触觉传感的硬件驱动局限，提出需求驱动框架，利用VR平台收集数据，识别社交触摸手势并构建数据集，分析得出定量基线，其方法可转移，能为特定形态机器人得出传感需求。
AI 中文摘要
社交物理人机交互（spHRI）的触觉传感是以硬件驱动方式设计的，预定义的传感器配置会限制覆盖范围、空间分辨率和可识别手势的范围。我们提出了一种需求驱动框架，可直接从交互数据中得出传感需求，特别是空间分辨率和放置位置。利用基于VR且有触觉反馈的平台，我们收集了多个社交场景中的高分辨率全身接触分布，从中识别出九种常见社交触摸手势。选择八种手势让18名参与者进行受控数据收集，得到了一个包含5520次试验的开源数据集。对接触分布和模拟触觉编码的分析为类人机器人平台上的皮肤覆盖和传感器密度提供了定量基线。虽然该方法在单个机器人平台上得到了验证，但它旨在可转移到其他机器人形态，有可能在硬件制造之前得出特定形态的传感需求。
英文摘要
Tactile sensing for social-physical human-robot interaction (spHRI) is designed in a hardware-driven manner, where predefined sensor configurations constrain coverage, spatial resolution, and the range of recognizable gestures. We propose a requirement-driven framework that derives sensing requirements, specifically spatial resolution and placement, directly from interaction data. Using a VR-based platform with haptic feedback, we collected high-resolution whole-body contact distributions across multiple social scenarios, from which we identified nine recurring social touch gestures. Eight gestures were selected for controlled data collection with 18 participants, yielding an open-source dataset of 5,520 trials. Analysis of contact distributions and simulated tactile encodings provides quantitative baselines for skin coverage and sensor density on a humanoid robot platform. While demonstrated on a single robot platform, the methodology is designed to be transferable to other robot morphologies, potentially enabling morphology-specific sensing requirements to be derived prior to hardware fabrication.
Comments8 pages, 6 figures, accepted to IROS 2026
