# ReSkin: versatile, replaceable, lasting tactile skins arXiv: https://arxiv.org/abs/2111.00071 ยท HTML: https://mlsanity.com/paper/2111.00071/ ## Key facts - Cited by 35 papers in the map (data through 14 Oct 2026). - Builds on 10 of its 36 references that are in the map. - First posted on arXiv 29 Oct 2021. ## Builds on (most cited first, 10 of 10) - [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020) - [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015) - [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) - [More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch](https://mlsanity.com/paper/1805.11085/) (2018) - [The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?](https://mlsanity.com/paper/1710.05512/) (2017) - [GelSlim: A High-Resolution, Compact, Robust, and Calibrated Tactile-sensing Finger](https://mlsanity.com/paper/1803.00628/) (2018) - [Unsupervised Learning of Spatiotemporally Coherent Metrics](https://mlsanity.com/paper/1412.6056/) (2014) - [GelSight Wedge: Measuring High-Resolution 3D Contact Geometry with a Compact Robot Finger](https://mlsanity.com/paper/2106.08851/) (2021) - [Slow and steady feature analysis: higher order temporal coherence in video](https://mlsanity.com/paper/1506.04714/) (2015) - [Robust Learning of Tactile Force Estimation through Robot Interaction](https://mlsanity.com/paper/1810.06187/) (2018) ## Built on by (most cited first, 30 of 35) - [Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation](https://mlsanity.com/paper/2503.02881/) (2025) - [On Bringing Robots Home](https://mlsanity.com/paper/2311.16098/) (2023) - [DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation](https://mlsanity.com/paper/2211.09423/) (2022) - [Rotating without Seeing: Towards In-hand Dexterity through Touch](https://mlsanity.com/paper/2303.10880/) (2023) - [Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play](https://mlsanity.com/paper/2303.12076/) (2023) - [See to Touch: Learning Tactile Dexterity through Visual Incentives](https://mlsanity.com/paper/2309.12300/) (2023) - [The Power of the Senses: Generalizable Manipulation from Vision and Touch through Masked Multimodal Learning](https://mlsanity.com/paper/2311.00924/) (2023) - [PressureVision: Estimating Hand Pressure from a Single RGB Image](https://mlsanity.com/paper/2203.10385/) (2022) - [AnySkin: Plug-and-play Skin Sensing for Robotic Touch](https://mlsanity.com/paper/2409.08276/) (2024) - [EgoPressure: A Dataset for Hand Pressure and Pose Estimation in Egocentric Vision](https://mlsanity.com/paper/2409.02224/) (2024) - [DEFT: Dexterous Fine-Tuning for Real-World Hand Policies](https://mlsanity.com/paper/2310.19797/) (2023) - [A Survey of Robotic Navigation and Manipulation with Physics Simulators in the Era of Embodied AI](https://mlsanity.com/paper/2505.01458/) (2025) - [Learning In-Hand Translation Using Tactile Skin With Shear and Normal Force Sensing](https://mlsanity.com/paper/2407.07885/) (2024) - [Hearing Touch: Audio-Visual Pretraining for Contact-Rich Manipulation](https://mlsanity.com/paper/2405.08576/) (2024) - [PolyTouch: A Robust Multi-Modal Tactile Sensor for Contact-rich Manipulation Using Tactile-Diffusion Policies](https://mlsanity.com/paper/2504.19341/) (2025) - [PressureVision++: Estimating Fingertip Pressure from Diverse RGB Images](https://mlsanity.com/paper/2301.02310/) (2023) - [Evetac: An Event-based Optical Tactile Sensor for Robotic Manipulation](https://mlsanity.com/paper/2312.01236/) (2023) - [Learning Precise, Contact-Rich Manipulation through Uncalibrated Tactile Skins](https://mlsanity.com/paper/2410.17246/) (2024) - [OSMO: Open-Source Tactile Glove for Human-to-Robot Skill Transfer](https://mlsanity.com/paper/2512.08920/) (2025) - [All the Feels: A dexterous hand with large-area tactile sensing](https://mlsanity.com/paper/2210.15658/) (2022) - [FlexiTac: A Low-Cost, Open-Source, Scalable Tactile Sensing Solution for Robotic Systems](https://mlsanity.com/paper/2604.28156/) (2026) - [TactoFind: A Tactile Only System for Object Retrieval](https://mlsanity.com/paper/2303.13482/) (2023) - [eFlesh: Highly customizable Magnetic Touch Sensing using Cut-Cell Microstructures](https://mlsanity.com/paper/2506.09994/) (2025) - [DexSkin: High-Coverage Conformable Robotic Skin for Learning Contact-Rich Manipulation](https://mlsanity.com/paper/2509.18830/) (2025) - [Imagine2touch: Predictive Tactile Sensing for Robotic Manipulation using Efficient Low-Dimensional Signals](https://mlsanity.com/paper/2405.01192/) (2024) - [Seeing Touch from Motion: A Unified Modality-Aware Visuo-Tactile Policy with Tactile Motion Correlation](https://mlsanity.com/paper/2606.29941/) (2026) - [Task-Optimized Convolutional Recurrent Networks Align with Tactile Processing in the Rodent Brain](https://mlsanity.com/paper/2505.18361/) (2025) - [PseudoTouch: Efficiently Imaging the Surface Feel of Objects for Robotic Manipulation](https://mlsanity.com/paper/2403.15107/) (2024) - [An Active Learning Framework with a Class Balancing Strategy for Time Series Classification](https://mlsanity.com/paper/2405.12122/) (2024) - [The Role of Tactile Sensing for Learning Reach and Grasp](https://mlsanity.com/paper/2502.20367/) (2025) ## Abstract (arXiv) Soft sensors have continued growing interest in robotics, due to their ability to enable both passive conformal contact from the material properties and active contact data from the sensor properties. However, the same properties of conformal contact result in faster deterioration of soft sensors and larger variations in their response characteristics over time and across samples, inhibiting their ability to be long-lasting and replaceable. ReSkin is a tactile soft sensor that leverages machine learning and magnetic sensing to offer a low-cost, diverse and compact solution for long-term use. Magnetic sensing separates the electronic circuitry from the passive interface, making it easier to replace interfaces as they wear out while allowing for a wide variety of form factors. Machine learning allows us to learn sensor response models that are robust to variations across fabrication and time, and our self-supervised learning algorithm enables finer performance enhancement with small, inexpensive data collection procedures. We believe that ReSkin opens the door to more versatile, scalable and inexpensive tactile sensation modules than existing alternatives.