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Related Experiment Video

Updated: Jun 13, 2026

Applying Incongruent Visual-Tactile Stimuli during Object Transfer with Vibro-Tactile Feedback
05:43

Applying Incongruent Visual-Tactile Stimuli during Object Transfer with Vibro-Tactile Feedback

Published on: May 23, 2019

Visual-Textual Information-Driven Tactile Data Generation Method.

Rui Song, Yang Xu, Zhangzheng Tu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 11, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Generating tactile data for embodied intelligence is challenging due to scarcity. The VTTac framework uses visual-textual data to create realistic tactile information, improving AI perception and interaction capabilities.

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    A Tactile Automated Passive-Finger Stimulator (TAPS)
    19:44

    A Tactile Automated Passive-Finger Stimulator (TAPS)

    Published on: June 3, 2009

    Area of Science:

    • Robotics and Artificial Intelligence
    • Computer Vision and Natural Language Processing

    Background:

    • Tactile data is crucial for embodied intelligence, enhancing environmental perception and interaction.
    • Acquiring tactile data is expensive and labor-intensive, leading to significant data scarcity.
    • Cross-modal generation using visual and textual data is a potential solution, but alignment is difficult under data-scarce conditions.

    Purpose of the Study:

    • To propose a novel framework, VTTac, for generating tactile data driven by visual and textual information.
    • To address the challenges of data scarcity and sparse annotations in cross-modal tactile data generation.
    • To improve the environmental perception and interaction capabilities of intelligent agents through synthetic tactile data.

    Main Methods:

    • Developed a visual-textual information-driven tactile data generation (VTTac) framework.
    • Implemented a multi-granularity text enhancement strategy for hierarchical semantic enrichment to combat annotation sparsity.
    • Designed a cascaded dual cross-attention mechanism for effective cross-modal alignment.
    • Utilized a condition adapter with a low-frequency background prior and wavelet transform for high-frequency texture synthesis and background fusion.

    Main Results:

    • VTTac consistently outperformed representative baselines across three datasets.
    • Synthesized tactile data demonstrated physical faithfulness in downstream tasks like material classification and semantic reasoning.
    • Zero-shot experiments confirmed the framework's generalization ability to unseen objects.

    Conclusions:

    • The VTTac framework effectively generates high-quality tactile data from visual and textual information, even with sparse annotations.
    • The generated tactile data is physically faithful and useful for downstream AI tasks, advancing embodied intelligence.
    • VTTac offers a viable solution to tactile data scarcity, enabling more robust AI perception and interaction.