eFlesh: Highly customizable Magnetic Touch Sensing using Cut-Cell Microstructures

• URL: http://arxiv.org/abs/2506.09994v1
• Date: Wed, 11 Jun 2025 17:59:46 GMT
• Title: eFlesh: Highly customizable Magnetic Touch Sensing using Cut-Cell Microstructures
• Authors: Venkatesh Pattabiraman, Zizhou Huang, Daniele Panozzo, Denis Zorin, Lerrel Pinto, Raunaq Bhirangi,
• Abstract summary: Building an eFlesh sensor requires only four components: a 3D printer, off-the-shelf magnets, a CAD model of the desired shape, and a magnetometer circuit board.<n>We provide an open-source design tool that converts convex OBJ/STL files into 3D-printable STLs for fabrication.<n>Our sensor characterization experiments demonstrate the capabilities of eFlesh: contact localization RMSE of 0.5 mm, and force prediction RMSE of 0.27 N for normal force and 0.12 N for shear force.
• Score: 35.94440287795584
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: If human experience is any guide, operating effectively in unstructured environments -- like homes and offices -- requires robots to sense the forces during physical interaction. Yet, the lack of a versatile, accessible, and easily customizable tactile sensor has led to fragmented, sensor-specific solutions in robotic manipulation -- and in many cases, to force-unaware, sensorless approaches. With eFlesh, we bridge this gap by introducing a magnetic tactile sensor that is low-cost, easy to fabricate, and highly customizable. Building an eFlesh sensor requires only four components: a hobbyist 3D printer, off-the-shelf magnets (<$5), a CAD model of the desired shape, and a magnetometer circuit board. The sensor is constructed from tiled, parameterized microstructures, which allow for tuning the sensor's geometry and its mechanical response. We provide an open-source design tool that converts convex OBJ/STL files into 3D-printable STLs for fabrication. This modular design framework enables users to create application-specific sensors, and to adjust sensitivity depending on the task. Our sensor characterization experiments demonstrate the capabilities of eFlesh: contact localization RMSE of 0.5 mm, and force prediction RMSE of 0.27 N for normal force and 0.12 N for shear force. We also present a learned slip detection model that generalizes to unseen objects with 95% accuracy, and visuotactile control policies that improve manipulation performance by 40% over vision-only baselines -- achieving 91% average success rate for four precise tasks that require sub-mm accuracy for successful completion. All design files, code and the CAD-to-eFlesh STL conversion tool are open-sourced and available on https://e-flesh.com.

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