Scalable Open-Source Visuotactile Sensor for 6-Axis Contact Wrench Estimation in Tensegrity Robots
Robotics
Summary
The authors designed a new touch sensor for tensegrity robots that can detect both forces and contact points in 3D space. Their sensor uses a special layered construction combining soft and rigid materials without glue, making it lightweight and easy to build for large robots. They trained a neural network to turn sensor readings into accurate measurements of forces and torques. Tests show their system works well even when the robot is moving and can reliably sense when the robot touches the ground. This open-source design helps improve robotic sense of touch in a cost-effective and understandable way.
Authors
Wenzhe Tong, Jonathan Mi, Xili Yi, Nima Fazeli, Xiaonan Huang
Abstract
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}