# Touch in the Wild: Learning Fine-Grained Manipulation with a Portable Visuo-Tactile Gripper

arXiv: https://arxiv.org/abs/2507.15062 · HTML: https://mlsanity.com/paper/2507.15062/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 54 references that are in the map.
- First posted on arXiv 20 Jul 2025.

## Builds on (most cited first, 30 of 43)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots](https://mlsanity.com/paper/2402.10329/) (2024)
- [One-Shot Visual Imitation Learning via Meta-Learning](https://mlsanity.com/paper/1709.04905/) (2017)
- [MimicPlay: Long-Horizon Imitation Learning by Watching Human Play](https://mlsanity.com/paper/2302.12422/) (2023)
- [Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks](https://mlsanity.com/paper/1810.10191/) (2018)
- [Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation](https://mlsanity.com/paper/2503.02881/) (2025)
- [More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch](https://mlsanity.com/paper/1805.11085/) (2018)
- [Binding Touch to Everything: Learning Unified Multimodal Tactile Representations](https://mlsanity.com/paper/2401.18084/) (2024)
- [Touch and Go: Learning from Human-Collected Vision and Touch](https://mlsanity.com/paper/2211.12498/) (2022)
- [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)
- [ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer](https://mlsanity.com/paper/2204.02389/) (2022)
- [UMI on Legs: Making Manipulation Policies Mobile with Manipulation-Centric Whole-body Controllers](https://mlsanity.com/paper/2407.10353/) (2024)
- [3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing](https://mlsanity.com/paper/2410.24091/) (2024)
- [See, Hear, and Feel: Smart Sensory Fusion for Robotic Manipulation](https://mlsanity.com/paper/2212.03858/) (2022)
- [The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?](https://mlsanity.com/paper/1710.05512/) (2017)
- [Connecting Touch and Vision via Cross-Modal Prediction](https://mlsanity.com/paper/1906.06322/) (2019)
- [Sparsh: Self-supervised touch representations for vision-based tactile sensing](https://mlsanity.com/paper/2410.24090/) (2024)
- [TACTO: A Fast, Flexible, and Open-source Simulator for High-Resolution Vision-based Tactile Sensors](https://mlsanity.com/paper/2012.08456/) (2020)
- [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)
- [AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors](https://mlsanity.com/paper/2502.12191/) (2025)
- [Visuo-Tactile Transformers for Manipulation](https://mlsanity.com/paper/2210.00121/) (2022)
- [Transferable Tactile Transformers for Representation Learning Across Diverse Sensors and Tasks](https://mlsanity.com/paper/2406.13640/) (2024)
- [Taxim: An Example-based Simulation Model for GelSight Tactile Sensors](https://mlsanity.com/paper/2109.04027/) (2021)
- [Dynamic Handover: Throw and Catch with Bimanual Hands](https://mlsanity.com/paper/2309.05655/) (2023)

## Built on by (most cited first, 29 of 29)

- [LLM-as-a-Verifier: A General-Purpose Verification Framework](https://mlsanity.com/paper/2607.05391/) (2026)
- [Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulation](https://mlsanity.com/paper/2606.08737/) (2026)
- [OPENTOUCH: Bringing Full-Hand Touch to Real-World Interaction](https://mlsanity.com/paper/2512.16842/) (2025)
- [Multi-Modal Manipulation via Multi-Modal Policy Consensus](https://mlsanity.com/paper/2509.23468/) (2025)
- [T-Rex: Tactile-Reactive Dexterous Manipulation](https://mlsanity.com/paper/2606.17055/) (2026)
- [AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perception](https://mlsanity.com/paper/2602.09617/) (2026)
- [VT-Refine: Learning Bimanual Assembly with Visuo-Tactile Feedback via Simulation Fine-Tuning](https://mlsanity.com/paper/2510.14930/) (2025)
- [OSMO: Open-Source Tactile Glove for Human-to-Robot Skill Transfer](https://mlsanity.com/paper/2512.08920/) (2025)
- [AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models](https://mlsanity.com/paper/2605.07308/) (2026)
- [FlexiTac: A Low-Cost, Open-Source, Scalable Tactile Sensing Solution for Robotic Systems](https://mlsanity.com/paper/2604.28156/) (2026)
- [Data Pyramid for Embodied Manipulation: A Survey](https://mlsanity.com/paper/2607.24744/) (2026)
- [Towards Human-Like Manipulation through RL-Augmented Teleoperation and Mixture-of-Dexterous-Experts VLA](https://mlsanity.com/paper/2603.08122/) (2026)
- [UMIGen: A Unified Framework for Egocentric Point Cloud Generation and Cross-Embodiment Robotic Imitation Learning](https://mlsanity.com/paper/2511.09302/) (2025)
- [Tactile-based Multimodal Fusion in Embodied Intelligence: A Survey of Vision, Language, and Contact-Driven Paradigms](https://mlsanity.com/paper/2605.17336/) (2026)
- [Mobile UMI: Cross-View Diffusion Policy with Decoupled Kinematics for Mobile Manipulation](https://mlsanity.com/paper/2605.20894/) (2026)
- [Seeing Touch from Motion: A Unified Modality-Aware Visuo-Tactile Policy with Tactile Motion Correlation](https://mlsanity.com/paper/2606.29941/) (2026)
- [τ: Learning Touch-Augmented Vision-Language-Action Models from Future Visual Supervision](https://mlsanity.com/paper/2607.24485/) (2026)
- [HapTile: A Haptic-Informed Vision-Tactile-Language-Action Dataset for Contact-Rich Imitation Learning](https://mlsanity.com/paper/2606.04825/) (2026)
- [Tac-DINO: Learning Vision-Tactile Features with Patch Alignment](https://mlsanity.com/paper/2606.12069/) (2026)
- [OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies](https://mlsanity.com/paper/2607.03723/) (2026)
- [TransDex: Pre-training Visuo-Tactile Policy with Point Cloud Reconstruction for Dexterous Manipulation of Transparent Objects](https://mlsanity.com/paper/2603.13869/) (2026)
- [Tac2Pix: Image-Space Visuo-Tactile Fusion for Dexterous Manipulation](https://mlsanity.com/paper/2606.08765/) (2026)
- [Deform360: A Massive Multi-view Visuotactile Dataset for Deformable World Models](https://mlsanity.com/paper/2607.05390/) (2026)
- [N0-Foundation: Towards the Age of Tactile Intelligence](https://mlsanity.com/paper/2608.29601/) (2026)
- [CosmoH2G: A Hand-to-Gripper Transfer Dataset and Baseline Method for Object Manipulation with Complex Spatial Movements](https://mlsanity.com/paper/2609.07498/) (2026)
- [SlipSense: Multimodal Tactile Learning for Low-Latency and Generalized Slip Detection](https://mlsanity.com/paper/2609.15910/) (2026)
- [DexTouch-WM: Learning Action-Conditioned Tactile World Models from Human Touch for Dexterous Robot Manipulation](https://mlsanity.com/paper/2609.20649/) (2026)
- [TouchScale: 500 Hours of Human Vision and Touch for Visual-Tactile Learning](https://mlsanity.com/paper/2610.10288/) (2026)
- [How Firm Should a Grasp Be?](https://mlsanity.com/paper/2610.11221/) (2026)

## Abstract (arXiv)

Handheld grippers are increasingly used to collect human demonstrations due to their ease of deployment and versatility. However, most existing designs lack tactile sensing, despite the critical role of tactile feedback in precise manipulation. We present a portable, lightweight gripper with integrated tactile sensors that enables synchronized collection of visual and tactile data in diverse, real-world, and in-the-wild settings. Building on this hardware, we propose a cross-modal representation learning framework that integrates visual and tactile signals while preserving their distinct characteristics. The learning procedure allows the emergence of interpretable representations that consistently focus on contacting regions relevant for physical interactions. When used for downstream manipulation tasks, these representations enable more efficient and effective policy learning, supporting precise robotic manipulation based on multimodal feedback. We validate our approach on fine-grained tasks such as test tube insertion and pipette-based fluid transfer, demonstrating improved accuracy and robustness under external disturbances. Our project page is available at https://binghao-huang.github.io/touch_in_the_wild/ .