SToLa: Self-Adaptive Touch-Language Framework with Tactile Commonsense Reasoning in Open-Ended Scenarios

• URL: http://arxiv.org/abs/2505.04201v1
• Date: Wed, 07 May 2025 07:55:35 GMT
• Title: SToLa: Self-Adaptive Touch-Language Framework with Tactile Commonsense Reasoning in Open-Ended Scenarios
• Abstract summary: This paper explores the challenges of integrating tactile sensing into intelligent systems for multimodal reasoning.<n>We introduce SToLa, a Self-language Touch-Language framework.<n>We present a comprehensive tactile commonsense reasoning dataset and benchmark.
• Score: 44.650313509143984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper explores the challenges of integrating tactile sensing into intelligent systems for multimodal reasoning, particularly in enabling commonsense reasoning about the open-ended physical world. We identify two key challenges: modality discrepancy, where existing large touch-language models often treat touch as a mere sub-modality of language, and open-ended tactile data scarcity, where current datasets lack the diversity, open-endness and complexity needed for reasoning. To overcome these challenges, we introduce SToLa, a Self-Adaptive Touch-Language framework. SToLa utilizes Mixture of Experts (MoE) to dynamically process, unify, and manage tactile and language modalities, capturing their unique characteristics. Crucially, we also present a comprehensive tactile commonsense reasoning dataset and benchmark featuring free-form questions and responses, 8 physical properties, 4 interactive characteristics, and diverse commonsense knowledge. Experiments show SToLa exhibits competitive performance compared to existing models on the PhysiCLeAR benchmark and self-constructed datasets, proving the effectiveness of the Mixture of Experts architecture in multimodal management and the performance advantages for open-scenario tactile commonsense reasoning tasks.

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