Multimodal Deep Learning for Perception and Interaction in Service Robots

With the rapid development of artificial intelligence technology, service robots are becoming increasingly widespread in daily life applications. This paper proposes a multimodal deep learning-based perception and interaction system for service robots that achieves more intelligent and natural human-robot interaction by fusing RGB images, depth information, and speech data. The system employs improved multimodal feature extraction networks, adaptive fusion strategies, and temporal modeling mechanisms, effectively enhancing the robot's perception accuracy and interaction naturalness in complex environments. Experimental results on multiple real-world scenario datasets demonstrate that the proposed method improves object recognition accuracy by 4.2%, interaction response speed by 15.8%, and achieves a user satisfaction score of 8.7 points while maintaining real-time performance. This research provides new technical solutions for service robot applications in smart homes, healthcare, and other domains.