Core Insights - The article discusses the advancements in multimodal fusion and vision-language models (VLMs) as essential tools for enhancing robot vision technology, emphasizing their potential in complex reasoning and long-term task decision-making [4][10]. Multimodal Fusion and Robot Vision - Multimodal fusion enhances semantic scene understanding by integrating various data sources, such as visual, linguistic, depth, and lidar information, addressing limitations faced by traditional unimodal methods [8][9]. - The rise of VLMs has propelled the development of multimodal fusion paradigms, showcasing capabilities in zero-shot understanding and instruction following [9][10]. Key Applications and Challenges - The article identifies key applications of multimodal fusion in tasks like simultaneous localization and mapping (SLAM), 3D object detection, navigation, and robot manipulation [10][19]. - Challenges in multimodal fusion include cross-modal alignment, efficient training strategies, and real-time performance optimization [10][19]. Data Sets and Benchmarking - A comprehensive analysis of mainstream multimodal datasets used for robot tasks is provided, detailing their modality combinations, task coverage, and limitations [10][43]. - The importance of high-quality multimodal datasets is highlighted, as they are crucial for model training and performance evaluation [62]. Future Directions - The article suggests future research directions to address challenges in multimodal fusion, such as improving cross-modal alignment techniques and enhancing real-time performance [10][63]. - Emphasis is placed on the need for standardized datasets and benchmarks to facilitate comparisons across different research efforts [66].