# Learning Precise, Contact-Rich Manipulation through Uncalibrated Tactile Skins

arXiv: https://arxiv.org/abs/2410.17246 · HTML: https://mlsanity.com/paper/2410.17246/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 49 references that are in the map.
- First posted on arXiv 22 Oct 2024.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [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)
- [General In-Hand Object Rotation with Vision and Touch](https://mlsanity.com/paper/2309.09979/) (2023)
- [Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks](https://mlsanity.com/paper/1907.13098/) (2019)
- [BAKU: An Efficient Transformer for Multi-Task Policy Learning](https://mlsanity.com/paper/2406.07539/) (2024)
- [Learning Visuotactile Skills with Two Multifingered Hands](https://mlsanity.com/paper/2404.16823/) (2024)
- [Teach a Robot to FISH: Versatile Imitation from One Minute of Demonstrations](https://mlsanity.com/paper/2303.01497/) (2023)
- [OPEN TEACH: A Versatile Teleoperation System for Robotic Manipulation](https://mlsanity.com/paper/2403.07870/) (2024)
- [See, Hear, and Feel: Smart Sensory Fusion for Robotic Manipulation](https://mlsanity.com/paper/2212.03858/) (2022)
- [Connecting Touch and Vision via Cross-Modal Prediction](https://mlsanity.com/paper/1906.06322/) (2019)
- [Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play](https://mlsanity.com/paper/2303.12076/) (2023)
- [Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing](https://mlsanity.com/paper/2312.01853/) (2023)
- [ReSkin: versatile, replaceable, lasting tactile skins](https://mlsanity.com/paper/2111.00071/) (2021)
- [See to Touch: Learning Tactile Dexterity through Visual Incentives](https://mlsanity.com/paper/2309.12300/) (2023)
- [MidasTouch: Monte-Carlo inference over distributions across sliding touch](https://mlsanity.com/paper/2210.14210/) (2022)
- [Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation](https://mlsanity.com/paper/2312.13469/) (2023)
- [Slip Detection with Combined Tactile and Visual Information](https://mlsanity.com/paper/1802.10153/) (2018)
- [RB2: Robotic Manipulation Benchmarking with a Twist](https://mlsanity.com/paper/2203.08098/) (2022)
- [AnySkin: Plug-and-play Skin Sensing for Robotic Touch](https://mlsanity.com/paper/2409.08276/) (2024)
- [Active Extrinsic Contact Sensing: Application to General Peg-in-Hole Insertion](https://mlsanity.com/paper/2110.03555/) (2021)
- [Learning to Identify Object Instances by Touch: Tactile Recognition via Multimodal Matching](https://mlsanity.com/paper/1903.03591/) (2019)
- [Tactile SLAM: Real-time inference of shape and pose from planar pushing](https://mlsanity.com/paper/2011.07044/) (2020)
- [Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning](https://mlsanity.com/paper/2210.02343/) (2022)
- [Tactile Pose Estimation and Policy Learning for Unknown Object Manipulation](https://mlsanity.com/paper/2203.10685/) (2022)
- [All the Feels: A dexterous hand with large-area tactile sensing](https://mlsanity.com/paper/2210.15658/) (2022)
- [Deformation Capture via Soft and Stretchable Sensor Arrays](https://mlsanity.com/paper/1804.04013/) (2018)

## Built on by (most cited first, 12 of 12)

- [Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation](https://mlsanity.com/paper/2503.02881/) (2025)
- [Touch begins where vision ends: Generalizable policies for contact-rich manipulation](https://mlsanity.com/paper/2506.13762/) (2025)
- [Feel the Force: Contact-Driven Learning from Humans](https://mlsanity.com/paper/2506.01944/) (2025)
- [eFlesh: Highly customizable Magnetic Touch Sensing using Cut-Cell Microstructures](https://mlsanity.com/paper/2506.09994/) (2025)
- [ARMOR: Egocentric Perception for Humanoid Robot Collision Avoidance and Motion Planning](https://mlsanity.com/paper/2412.00396/) (2024)
- [ContactWorld: What Representations Matter for Vision-Tactile Latent World Models in Contact-Rich Manipulation](https://mlsanity.com/paper/2606.13877/) (2026)
- [Modular Sensory Stream for Integrating Physical Feedback in Vision-Language-Action Models](https://mlsanity.com/paper/2604.23272/) (2026)
- [Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning](https://mlsanity.com/paper/2603.26660/) (2026)
- [Tac-DINO: Learning Vision-Tactile Features with Patch Alignment](https://mlsanity.com/paper/2606.12069/) (2026)
- [Tac2Pix: Image-Space Visuo-Tactile Fusion for Dexterous Manipulation](https://mlsanity.com/paper/2606.08765/) (2026)
- [Learning tactile perception from high-bandwidth single-point sensing](https://mlsanity.com/paper/2609.24621/) (2026)
- [SoTa: Soft Tactile Skins for Dexterous Manipulation](https://mlsanity.com/paper/2610.02338/) (2026)

## Abstract (arXiv)

While visuomotor policy learning has advanced robotic manipulation, precisely executing contact-rich tasks remains challenging due to the limitations of vision in reasoning about physical interactions. To address this, recent work has sought to integrate tactile sensing into policy learning. However, many existing approaches rely on optical tactile sensors that are either restricted to recognition tasks or require complex dimensionality reduction steps for policy learning. In this work, we explore learning policies with magnetic skin sensors, which are inherently low-dimensional, highly sensitive, and inexpensive to integrate with robotic platforms. To leverage these sensors effectively, we present the Visuo-Skin (ViSk) framework, a simple approach that uses a transformer-based policy and treats skin sensor data as additional tokens alongside visual information. Evaluated on four complex real-world tasks involving credit card swiping, plug insertion, USB insertion, and bookshelf retrieval, ViSk significantly outperforms both vision-only and optical tactile sensing based policies. Further analysis reveals that combining tactile and visual modalities enhances policy performance and spatial generalization, achieving an average improvement of 27.5% across tasks. https://visuoskin.github.io/