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

arXiv: https://arxiv.org/abs/2506.09994 · HTML: https://mlsanity.com/paper/2506.09994/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 69 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 30 of 36)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset](https://mlsanity.com/paper/2403.12945/) (2024)
- [Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots](https://mlsanity.com/paper/2402.10329/) (2024)
- [RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking](https://mlsanity.com/paper/2309.01918/) (2023)
- [DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation](https://mlsanity.com/paper/2005.14679/) (2020)
- [Data Scaling Laws in Imitation Learning for Robotic Manipulation](https://mlsanity.com/paper/2410.18647/) (2024)
- [More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch](https://mlsanity.com/paper/1805.11085/) (2018)
- [On Bringing Robots Home](https://mlsanity.com/paper/2311.16098/) (2023)
- [Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulation](https://mlsanity.com/paper/2307.03659/) (2023)
- [BAKU: An Efficient Transformer for Multi-Task Policy Learning](https://mlsanity.com/paper/2406.07539/) (2024)
- [3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing](https://mlsanity.com/paper/2410.24091/) (2024)
- [OPEN TEACH: A Versatile Teleoperation System for Robotic Manipulation](https://mlsanity.com/paper/2403.07870/) (2024)
- [Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing](https://mlsanity.com/paper/2312.01853/) (2023)
- [Coupling Vision and Proprioception for Navigation of Legged Robots](https://mlsanity.com/paper/2112.02094/) (2021)
- [Transferable Tactile Transformers for Representation Learning Across Diverse Sensors and Tasks](https://mlsanity.com/paper/2406.13640/) (2024)
- [Robot Utility Models: General Policies for Zero-Shot Deployment in New Environments](https://mlsanity.com/paper/2409.05865/) (2024)
- [GelSlim3.0: High-Resolution Measurement of Shape, Force and Slip in a Compact Tactile-Sensing Finger](https://mlsanity.com/paper/2103.12269/) (2021)
- [ReSkin: versatile, replaceable, lasting tactile skins](https://mlsanity.com/paper/2111.00071/) (2021)
- [GelSight Wedge: Measuring High-Resolution 3D Contact Geometry with a Compact Robot Finger](https://mlsanity.com/paper/2106.08851/) (2021)
- [Slip Detection with Combined Tactile and Visual Information](https://mlsanity.com/paper/1802.10153/) (2018)
- [AnySkin: Plug-and-play Skin Sensing for Robotic Touch](https://mlsanity.com/paper/2409.08276/) (2024)
- [Learning to Identify Object Instances by Touch: Tactile Recognition via Multimodal Matching](https://mlsanity.com/paper/1903.03591/) (2019)
- [Hierarchical State Space Models for Continuous Sequence-to-Sequence Modeling](https://mlsanity.com/paper/2402.10211/) (2024)
- [Learning In-Hand Translation Using Tactile Skin With Shear and Normal Force Sensing](https://mlsanity.com/paper/2407.07885/) (2024)
- [Learning Precise, Contact-Rich Manipulation through Uncalibrated Tactile Skins](https://mlsanity.com/paper/2410.17246/) (2024)
- [Two-Scale Topology Optimization with Microstructures](https://mlsanity.com/paper/1706.03189/) (2017)
- [All the Feels: A dexterous hand with large-area tactile sensing](https://mlsanity.com/paper/2210.15658/) (2022)
- [CushSense: Soft, Stretchable, and Comfortable Tactile-Sensing Skin for Physical Human-Robot Interaction](https://mlsanity.com/paper/2405.03155/) (2024)

## Built on by (most cited first, 5 of 5)

- [OSMO: Open-Source Tactile Glove for Human-to-Robot Skill Transfer](https://mlsanity.com/paper/2512.08920/) (2025)
- [FlexiTac: A Low-Cost, Open-Source, Scalable Tactile Sensing Solution for Robotic Systems](https://mlsanity.com/paper/2604.28156/) (2026)
- [Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning](https://mlsanity.com/paper/2603.26660/) (2026)
- [Ergodic Control and Controlled Diffusion for Robot Learning: Review and Tutorial](https://mlsanity.com/paper/2609.13295/) (2026)
- [Learning tactile perception from high-bandwidth single-point sensing](https://mlsanity.com/paper/2609.24621/) (2026)

## Abstract (arXiv)

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.