# Touch100k: A Large-Scale Touch-Language-Vision Dataset for Touch-Centric Multimodal Representation

arXiv: https://arxiv.org/abs/2406.03813 · HTML: https://mlsanity.com/paper/2406.03813/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2024.

## Built on by (most cited first, 27 of 27)

- [AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors](https://mlsanity.com/paper/2502.12191/) (2025)
- [TLA: Tactile-Language-Action Model for Contact-Rich Manipulation](https://mlsanity.com/paper/2503.08548/) (2025)
- [OmniVTLA: Vision-Tactile-Language-Action Models with Semantic-Aligned Tactile Sensing](https://mlsanity.com/paper/2508.08706/) (2025)
- [AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perception](https://mlsanity.com/paper/2602.09617/) (2026)
- [UniTac: A Unified Multimodal Model for Cross-Sensor Tactile Understanding and Generation](https://mlsanity.com/paper/2606.31451/) (2026)
- [X-Capture: An Open-Source Portable Device for Multi-Sensory Learning](https://mlsanity.com/paper/2504.02318/) (2025)
- [DECO: Decoupled Multimodal Diffusion Transformer for Bimanual Dexterous Manipulation with a Plugin Tactile Adapter](https://mlsanity.com/paper/2602.05513/) (2026)
- [Tactile Modality Fusion for Vision-Language-Action Models](https://mlsanity.com/paper/2603.14604/) (2026)
- [SToLa: Self-Adaptive Touch-Language Framework with Tactile Commonsense Reasoning in Open-Ended Scenarios](https://mlsanity.com/paper/2505.04201/) (2025)
- [Nature's Insight: A Novel Framework and Comprehensive Analysis of Agentic Reasoning Through the Lens of Neuroscience](https://mlsanity.com/paper/2505.05515/) (2025)
- [Tactile MNIST: Benchmarking Active Tactile Perception](https://mlsanity.com/paper/2506.06361/) (2025)
- [HapticCap: A Multimodal Dataset and Task for Understanding User Experience of Vibration Haptic Signals](https://mlsanity.com/paper/2507.13318/) (2025)
- [TacCoRL: Integrating Tactile Feedback into VLA via Simulation](https://mlsanity.com/paper/2606.11743/) (2026)
- [RA-Touch: Retrieval-Augmented Touch Understanding with Enriched Visual Data](https://mlsanity.com/paper/2505.14270/) (2025)
- [Collaborative Representation Learning for Alignment of Tactile, Language, and Vision Modalities](https://mlsanity.com/paper/2511.11512/) (2025)
- [TaCo: A Benchmark for Lossless and Lossy Codecs of Heterogeneous Tactile Data](https://mlsanity.com/paper/2602.09893/) (2026)
- [Seeing Through Touch: Tactile-Driven Visual Localization of Material Regions](https://mlsanity.com/paper/2604.11579/) (2026)
- [Tactile-based Multimodal Fusion in Embodied Intelligence: A Survey of Vision, Language, and Contact-Driven Paradigms](https://mlsanity.com/paper/2605.17336/) (2026)
- [τ: Learning Touch-Augmented Vision-Language-Action Models from Future Visual Supervision](https://mlsanity.com/paper/2607.24485/) (2026)
- [Touch-R1: Reinforcing Touch Reasoning in MLLMs](https://mlsanity.com/paper/2605.27154/) (2026)
- [Tac-DINO: Learning Vision-Tactile Features with Patch Alignment](https://mlsanity.com/paper/2606.12069/) (2026)
- [HapticLLaMA: A Multimodal Sensory Language Model for Haptic Captioning](https://mlsanity.com/paper/2508.06475/) (2025)
- [When Sensing Varies with Contexts: Context Probing for Tactile Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2603.25115/) (2026)
- [RCT: A Robot-Collected Touch-Vision-Language Dataset for Tactile Generalization](https://mlsanity.com/paper/2606.31694/) (2026)
- [Wake up for Touch! Mask-isolated Tactile Alignment Learning in MLLMs](https://mlsanity.com/paper/2607.00302/) (2026)
- [The Potential of Haptic Foundation Models](https://mlsanity.com/paper/2608.28664/) (2026)
- [Language-Guided Representation Learning for Robust Cross-Sensor Material Recognition](https://mlsanity.com/paper/2609.14783/) (2026)

## Abstract (arXiv)

Touch holds a pivotal position in enhancing the perceptual and interactive capabilities of both humans and robots. Despite its significance, current tactile research mainly focuses on visual and tactile modalities, overlooking the language domain. Inspired by this, we construct Touch100k, a paired touch-language-vision dataset at the scale of 100k, featuring tactile sensation descriptions in multiple granularities (i.e., sentence-level natural expressions with rich semantics, including contextual and dynamic relationships, and phrase-level descriptions capturing the key features of tactile sensations). Based on the dataset, we propose a pre-training method, Touch-Language-Vision Representation Learning through Curriculum Linking (TLV-Link, for short), inspired by the concept of curriculum learning. TLV-Link aims to learn a tactile representation for the GelSight sensor and capture the relationship between tactile, language, and visual modalities. We evaluate our representation's performance across two task categories (namely, material property identification and robot grasping prediction), focusing on tactile representation and zero-shot touch understanding. The experimental evaluation showcases the effectiveness of our representation. By enabling TLV-Link to achieve substantial improvements and establish a new state-of-the-art in touch-centric multimodal representation learning, Touch100k demonstrates its value as a valuable resource for research. Project page: https://cocacola-lab.github.io/Touch100k/.