Visual-Tactile Perception for Intelligent Robots: Typical Tasks and Datasets

Visual-tactile perception is fundamental to enabling intelligent robots to recognize and adaptively interact with dynamic environments. However, the development of multimodal perception algorithms faces a key challenge: the lack of largescale, standardized datasets required for robust optimization, which is exacerbated by the wide variation in existing datasets' scale, quality, and domain specificity. This paper surveys the field of visual-tactile perception in robotics, focusing on three core tasks and evaluates the performance of integrated algorithmic approaches. To address the dataset gap, we systematically review and categorize 21 publicly available visual-tactile datasets, dividing them into numerical (e.g., LMT, PHAC-2) and image-based (e.g., GelFabric) types. We analyze their collection methodologies, structural composition, and application contexts, comparing the strengths and limitations of each type. Finally, we discuss future development trends in dataset creation and multimodal perception. This work serves as a foundational reference for visualtactile perception research and provides the first comprehensive survey dedicated to visual-tactile datasets in intelligent robotics, aiming to guide future data-driven research and algorithmic design.