TouchFormer: A Robust Transformer-based Framework for Multimodal Material Perception

• URL: http://arxiv.org/abs/2511.19509v1
• Date: Mon, 24 Nov 2025 00:43:59 GMT
• Title: TouchFormer: A Robust Transformer-based Framework for Multimodal Material Perception
• Authors: Kailin Lyu, Long Xiao, Jianing Zeng, Junhao Dong, Xuexin Liu, Zhuojun Zou, Haoyue Yang, Lin Shu, Jie Hao,
• Abstract summary: We propose a robust multimodal fusion framework, TouchFormer.<n>We employ a Modality-Adaptive Gating mechanism and intra- and inter-modality attention mechanisms to adaptively integrate cross-modal features.<n>We show that TouchFormer achieves classification accuracy improvements of 2.48% and 6.83% on SSMC and subcategory tasks, respectively.
• Score: 8.939880394166348
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Traditional vision-based material perception methods often experience substantial performance degradation under visually impaired conditions, thereby motivating the shift toward non-visual multimodal material perception. Despite this, existing approaches frequently perform naive fusion of multimodal inputs, overlooking key challenges such as modality-specific noise, missing modalities common in real-world scenarios, and the dynamically varying importance of each modality depending on the task. These limitations lead to suboptimal performance across several benchmark tasks. In this paper, we propose a robust multimodal fusion framework, TouchFormer. Specifically, we employ a Modality-Adaptive Gating (MAG) mechanism and intra- and inter-modality attention mechanisms to adaptively integrate cross-modal features, enhancing model robustness. Additionally, we introduce a Cross-Instance Embedding Regularization(CER) strategy, which significantly improves classification accuracy in fine-grained subcategory material recognition tasks. Experimental results demonstrate that, compared to existing non-visual methods, the proposed TouchFormer framework achieves classification accuracy improvements of 2.48% and 6.83% on SSMC and USMC tasks, respectively. Furthermore, real-world robotic experiments validate TouchFormer's effectiveness in enabling robots to better perceive and interpret their environment, paving the way for its deployment in safety-critical applications such as emergency response and industrial automation. The code and datasets will be open-source, and the videos are available in the supplementary materials.

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