Universal Visuo-Tactile Video Understanding for Embodied Interaction

TL;DR

This paper introduces VTV-LLM, a multi-modal large language model that integrates visual and tactile video data for enhanced embodied interaction and tactile understanding.

Contribution

It presents the first visuo-tactile video understanding model with a new dataset and a three-stage training paradigm for cross-modal tactile reasoning.

Findings

VTV-LLM outperforms existing models in tactile video understanding tasks.

The dataset VTV150K enables diverse tactile attribute annotations.

The framework supports complex tactile reasoning and decision making.

Abstract

Tactile perception is essential for embodied agents to understand physical attributes of objects that cannot be determined through visual inspection alone. While existing approaches have made progress in visual and language modalities for physical understanding, they fail to effectively incorporate tactile information that provides crucial haptic feedback for real-world interaction. In this paper, we present VTV-LLM, the first multi-modal large language model for universal Visuo-Tactile Video (VTV) understanding that bridges the gap between tactile perception and natural language. To address the challenges of cross-sensor and cross-modal integration, we contribute VTV150K, a comprehensive dataset comprising 150,000 video frames from 100 diverse objects captured across three different tactile sensors (GelSight Mini, DIGIT, and Tac3D), annotated with four fundamental tactile attributes (hardness, protrusion, elasticity, and friction). We develop a novel three-stage training paradigm that includes VTV enhancement for robust visuo-tactile representation, VTV-text alignment for cross-modal correspondence, and text prompt finetuning for natural language generation. Our framework enables sophisticated tactile reasoning capabilities including feature assessment, comparative analysis, scenario-based decision making and so on. Experimental evaluations demonstrate that VTV-LLM achieves superior performance in tactile video understanding tasks, establishing a foundation for more intuitive human-machine interaction in tactile domains.