TacReasoner: A Dynamic Tactile-Language Framework for Interactive Reasoning in Real-World Scenarios
2026-07-06 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors focus on improving how machines understand touch by addressing two problems: difficulty in processing changing touch signals over time and errors in reasoning caused by lack of clear thinking steps. They introduce TacReasoner, which better interprets dynamic touch data and uses a new dataset called TouchCoT-10k to help machines reason step-by-step about tactile information. Tests show their method performs well compared to existing models, even those that are bigger and more complex. This work helps machines better understand and think about touch in everyday situations.
tactile sensingdynamic tactile signalsmultimodal reasoningtactile-language frameworkchain-of-thought reasoningTacReasonerTouchCoT-10kcommonsense reasoninginteractive reasoningfoundation models
Authors
Kailin Lyu, Di Wu, Long Xiao, Jianning Zeng, Jianwei He, Chang Lin, Lianyu Hu, Lin Shu, Jie Hao, Ce Hao
Abstract
Among the five primary human senses, tactile is arguably the most fundamental to survival, as it enables the perception of physical contact and interaction in real-world environments. In this paper, we explore two key challenges of integrating tactile sensing into intelligent systems for multimodal reasoning: (i) insufficient modeling of dynamic tactile signals, which restricts reasoning over temporally evolving properties, and (ii) hallucination in tactile foundation models caused by the absence of explicit reasoning mechanisms, leading to unstable real-world inference. To address these challenges, we propose TacReasoner, a dynamic tactile-language framework for interactive reasoning in real-world scenarios. First, TacReasoner incorporates a Dynamic-aware Tactile Encoder to enhance the perception and representation of dynamic tactile signals. More importantly, we introduce TouchCoT-10k, the first tactile chain-of-thought dataset for structured reasoning over tactile inputs. Upon it, we establish DynTac-Bench to systematically evaluate dynamic tactile perception and real-world commonsense reasoning. Experimental results demonstrate that TacReasoner achieves competitive performance against state-of-the-art models across multiple datasets. Notably, despite using only 7B parameters, TacReasoner outperforms the 14B VTV-LLM model on most subtasks, highlighting its effectiveness and efficiency in tactile commonsense reasoning.