# Learning Heterogeneous Tactile Representation for General Dexterous Manipulation

**Authors:** Bing Han, Yichuan Peng, Haoyu Dong, Yuanmeng Hu, Yongyi Jia, Fangjing Wang, Kecheng Zheng, Yiming Zhang, Yanhong Zeng, Xiaoqing Zhang, Chen Song, Rundong Zou, Anqi Dong, Yuan Guan, Ka Leong Cheng, Xing Zhu, Yujun Shen, Nan Xue, Wei Ning  
**Lab:** Robbyant · **Published:** 9 Nov 2026 · **Upvotes:** 92 · **Level:** Intermediate  
**Links:** [arXiv](https://arxiv.org/abs/2611.12806) · [PDF](https://arxiv.org/pdf/2611.12806) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.12806.html)

## Curator's notes

**TL;DR:** LingBot-Touch learns a unified tactile representation that works across different sensor types and improves dexterous manipulation.

- **What's new:** It creates sensor-agnostic tactile tokens that can be plugged into various policy models, unlike prior work tied to specific sensors.
- **Key result:** The unified tactile representation consistently enhances manipulation dexterity across simulation and real-robot tasks while remaining computationally efficient.
- **Why it matters:** Developers can reuse one tactile model for many sensors, simplifying system integration and boosting robot skill performance.

## Abstract

Tactile sensing is an indispensable modality for contact-rich dexterous manipulation, a capability that hints at the ultimate frontiers of embodied intelligence. However, commercially available tactile sensors are still at a relatively early stage of development, with each type of sensor offering irreplaceable strengths alongside its own drawbacks. A key challenge is thus to free tactile understanding from sensor-specific physics, so that embodied models can effectively leverage heterogeneous tactile data. To this end, we develop LingBot-Touch, a tactile foundation model characterized by the following properties. (i) Our model yields a unified representation across different tactile sensors (i.e., piezoresistive, magnetic, and visuotactile sensors), and is easily extensible to new sensor types. (ii) The resulting tactile tokens are plug-and-play: they can be seamlessly integrated into policy models across paradigms, from diffusion policy to vision-language-action (VLA) models such as π_0.5 and LingBot-VLA, consistently enhancing manipulation dexterity. (iii) Extensive experiments, spanning simulation and real-robot deployments across multiple hardware platforms, demonstrate that our tactile representation substantially benefits complex manipulation tasks while maintaining strong computational efficiency. We believe the findings in this work demonstrate the potential of leveraging cross-sensor data to scale tactile learning, and offer encouraging insights for the design of future hardware and embodied models.

## What you could build

1. Integrate LingBot-Touch tokens into a diffusion policy for object reorientation.
2. Add tactile tokens to a vision‑language‑action model for pick‑and‑place with unknown objects.
3. Create a sensor‑agnostic tactile dataset collector for benchmarking new tactile hardware.

## Quiz yourself

What three types of tactile sensors does LingBot-Touch unify in its representation?

**Answer:** It unifies piezoresistive, magnetic, and visuotactile sensors.

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Notes written by AI from the paper, numbers copied from the abstract. Check the paper before you rely on them. From AI Papers Daily.