为张拉整体机器人设计可扩展的开源触觉传感器，实现六维接触力估计算法。
Scalable Open-Source Visuotactile Sensor for 6-Axis Contact Wrench Estimation in Tensegrity Robots
- 采用3D打印TPU与弹性体一体成型结构，无需粘合剂实现耐用连接。
- 神经网络将触觉矢量场映射为六维力矩，静态数据误差均方根0.1531。
- 已集成于12公斤张拉整体机器人，支持地面接触识别与动态泛化能力。
本文提出一种可扩展、开源的视觉触觉传感系统，用于张拉整体机器人的六轴接触力矩估计与接触检测。所设计的末端传感器包含弹性外壳、3D打印热塑性聚氨酯（TPU）接口及内置相机与环形补光灯的刚性基座。创新性地引入晶格状内衬粘接技术，无需胶水即可形成牢固的弹性体-TPU界面，实现轻量化与模块化设计，兼容大规模张拉整体结构。基于触觉到力矩的神经网络模型，将剪切矢量场映射为六维力与力矩测量结果。实验表明，该系统在静态验证数据上达到0.1531的均方误差，且在动态运动中具备出色的域外泛化能力。将该系统完整集成于12公斤级张拉整体机器人后，成功可靠识别地面接触。该系统显著提升张拉整体机器人触觉反馈的实用性，提供低成本、可复现且物理可解释的接触感知与状态估计路径。开源代码见：github.com/Jonathan-Twz/tensegrity-gelfoot。
原文摘要 · Abstract (English)
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}
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