发表机构
University of Michigan（密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对张拉整体机器人提出可扩展开源视觉触觉传感系统，通过集成弹性体外壳等部件及螺旋状填充粘结技术，利用触觉到扳手的神经网络实现六轴力扳手估计，实验验证其准确性和稳定性，提升了机器人触觉反馈实用性。
AI 中文摘要
本文提出了一种用于张拉整体机器人的可扩展开源视觉触觉传感系统，可实现六轴力扳手估计和接触检测。所提出的端盖传感器集成了弹性体外壳、3D打印热塑性聚氨酯（TPU）接口和刚性底座，底座内有嵌入式摄像头和LED照明环。引入了一种新颖的螺旋状填充粘结技术，无需粘合剂即可形成耐用的弹性体-TPU接口，实现了与大型张拉整体结构兼容的轻质模块化设计。通过触觉到扳手的神经网络将剪切矢量场映射到六维力和扭矩测量。实验结果表明，在静态验证数据上的均方误差（MSE）为0.1531，在动态运动下具有准确稳定的扳手估计和域外泛化能力。此外，在12kg张拉整体机器人上的全系统集成证实了该传感器可靠识别地面接触的能力。该系统大大提高了张拉整体机器人触觉反馈的实用性，为接触感知本体感觉和状态估计提供了低成本、可重复且具有物理可解释性的途径。开源文件可在指定网址获取。
英文摘要
This paper presents a scalable, open-source visuotactile sensing system for tensegrity robots that enables six-axis wrench estimation and contact detection. The proposed endcap sensor integrates an elastomeric shell, a 3D-printed thermoplastic polyurethane (TPU) interface, and a rigid base housing an embedded camera and LED illumination ring. A novel gyroid-infill bonding technique is introduced to form a durable elastomer-TPU interface without adhesives, yielding a lightweight and modular design compatible with large-scale tensegrity structures. A tactile-to-wrench neural network maps shear vector fields to six-dimensional force and torque measurements. Experimental results demonstrate accurate and stable wrench estimation with a mean squared error (MSE) of 0.1531 on static validation data and out-of-domain generalization under dynamic motion. Furthermore, full-system integration on a 12 kg tensegrity robot confirms the sensor's ability to reliably identify ground contacts. The system substantially improves the practicality of tactile feedback for tensegrity robots, offering a low-cost, reproducible, and physically interpretable pathway toward contact-aware proprioception and state estimation. Open source files are available at \href{https://github.com/Jonathan-Twz/tensegrity-gelfoot}{github.com/Jonathan-Twz/tensegrity-gelfoot}
Comments8 pages, 10 figures, IROS 2026
