Language-Guided Representation Learning for Robust Cross-Sensor Material Recognition

• URL: http://arxiv.org/abs/2609.14783v1
• Date: Sun, 13 Sep 2026 20:53:14 GMT
• Title: Language-Guided Representation Learning for Robust Cross-Sensor Material Recognition
• Abstract summary: We propose a language-guided distillation framework for learning sensor-robust tactile representations.<n>We construct a 39K-sample touch-language dataset with human-annotated material labels.<n>We train a tactile encoder to align sensor-specific tactile images with language embeddings in a shared semantic space.
• Score: 1.441595027675735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robots need touch to manipulate objects safely and reliably, as many properties, such as softness, texture, and contact stability, are hard to infer from vision alone. However, vision-based tactile sensors yield different observations of the same material due to variations in optics, elastomer properties, and illumination, leading to poor generalization when trained on a single or multiple sensors. We propose a language-guided distillation framework for learning sensor-robust tactile representations. Language encodes high-level semantic properties of touch (e.g., rough, soft, slippery) that remain invariant across sensing hardware, providing a natural sensor-agnostic supervisory signal. We construct a 39K-sample touch-language dataset with human-annotated material labels and train a tactile encoder to align sensor-specific tactile images with language embeddings in a shared semantic space. We evaluate our approach for few-shot learning and cross-sensor transfer and benchmark it on six existing tactile datasets. Our method achieves 95% accuracy in the 100-shot setting, improves cross-sensor transfer by an average of 13.3% accuracy, and yields up to 19% accuracy gains across six existing tactile datasets. These results demonstrate that language-guided distillation enables scalable and hardware-agnostic tactile representation learning. Code and dataset are available at https://mashood3624.github.io/Language_Tactile/

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