This paper introduces HapticVLM, a novel multimodal system that integrates vision-language reasoning with deep convolutional networks to enable real-time haptic feedback. HapticVLM leverages a ConvNeXt-based material recognition module to generate robust visual embeddings for accurate identification of object materials, while a state-of-the-art Vision-Language Model (Qwen2-VL-2B-Instruct) infers ambient temperature from environmental cues. The system synthesizes tactile sensations by delivering vibrotactile feedback through speakers and thermal cues via a Peltier module, thereby bridging the gap between visual perception and tactile experience. Experimental evaluations demonstrate an average recognition accuracy of 84.67% across five distinct auditory-tactile patterns and a temperature estimation accuracy of 86.7% based on a tolerance-based evaluation method with an 8°C margin of error across 15 scenarios. Although promising, the current study is limited by the use of a small set of prominent patterns and a modest participant pool. Future work will focus on expanding the range of tactile patterns and increasing user studies to further refine and validate the system's performance. Overall, HapticVLM presents a significant step toward context-aware, multimodal haptic interaction with potential applications in virtual reality, and assistive technologies.
View on arXiv@article{khan2025_2505.02569,
title={ HapticVLM: VLM-Driven Texture Recognition Aimed at Intelligent Haptic Interaction },
author={ Muhammad Haris Khan and Miguel Altamirano Cabrera and Dmitrii Iarchuk and Yara Mahmoud and Daria Trinitatova and Issatay Tokmurziyev and Dzmitry Tsetserukou },
journal={arXiv preprint arXiv:2505.02569},
year={ 2025 }
}