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

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arxiv 2506.09994 v1 pith:2TLI4SK3 submitted 2025-06-11 cs.RO cs.AI

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

classification cs.RO cs.AI
keywords sensorefleshcustomizabledesignforceaccuracyfilesfour
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. TactX: Learning Shared Tactile Representations Across Diverse Sensors

    cs.RO 2026-06 unverdicted novelty 6.0

    TactX learns a shared latent representation across three tactile sensor modalities via joint training on paired contacts, enabling zero-shot policy transfer and higher success on pick-and-place, insertion, wiping, and...

  2. Deformable In-Hand Slip-Aware Tactile Sensor with Integrated Velocity, Force/Torque, and Pressure Map Sensing

    cs.RO 2026-06 unverdicted novelty 6.0

    Presents a deformable tactile sensor integrating velocity, force/torque, and pressure map sensing in a single compliant structure for in-hand manipulation.

  3. TacO: Benchmarking Tactile Sensors for Object Manipulation

    cs.RO 2026-05 unverdicted novelty 6.0

    The paper provides a task-driven benchmark comparing visual, acoustic, magnetic, and resistive tactile sensors on three manipulation tasks and concludes that sensor utility depends on modality, material friction, and ...

  4. A Sensorised Lattice Footplate for a Semi-Active Prosthetic Foot

    cs.RO 2026-06 unverdicted novelty 5.0

    Prototype integrates embedded magnetic sensing in a 3D-printed lattice footplate with a servo hydraulic damper, showing force tracking and separable loading in bench tests for semi-active prosthetic control.

  5. FlexiTac: A Low-Cost, Open-Source, Scalable Tactile Sensing Solution for Robotic Systems

    cs.RO 2026-04 unverdicted novelty 5.0

    FlexiTac is a scalable piezoresistive tactile sensing system with flexible FPC-Velostat-FPC pads and a 100 Hz multi-channel readout board that mounts on rigid or soft grippers and supports visuo-tactile learning.