Robotic Perception with a Large Tactile-Vision-Language Model for Physical Property Inference
Abstract
Inferring physical properties can significantly enhance robotic manipulation by enabling robots to handle objects safely and efficiently through adaptive grasping strategies. Previous approaches have typically relied on either tactile or visual data, limiting their ability to fully capture properties. We introduce a novel cross-modal perception framework that integrates visual observations with tactile representations within a multimodal vision-language model. Our physical reasoning framework, which employs a hierarchical feature alignment mechanism and a refined prompting strategy, enables our model to make property-specific predictions that strongly correlate with ground-truth measurements. Evaluated on 35 diverse objects, our approach outperforms existing baselines and demonstrates strong zero-shot generalization. Keywords: tactile perception, visual-tactile fusion, physical property inference, multimodal integration, robot perception
Cite
@article{arxiv.2506.19303,
title = {Robotic Perception with a Large Tactile-Vision-Language Model for Physical Property Inference},
author = {Zexiang Guo and Hengxiang Chen and Xinheng Mai and Qiusang Qiu and Gan Ma and Zhanat Kappassov and Qiang Li and Nutan Chen},
journal= {arXiv preprint arXiv:2506.19303},
year = {2025}
}
Comments
This paper has been accepted by the 2025 International Conference on Climbing and Walking Robots (CLAWAR). These authors contributed equally to this work: Zexiang Guo, Hengxiang Chen, Xinheng Mai