Multimodal Deep Learning for Emotionally Intelligent Human-Robot Interaction

As human-robot interaction (HRI) expands across healthcare, education, and customer service, the need for emotionally intelligent robots grows increasingly vital. This study explores how multimodal deep learning can enhance emotional intelligence in HRI by enabling robots to perceive and respond to human emotions with empathy and context awareness. The proposed system fuses visual (facial expressions, gestures), auditory (tone, pitch), and textual (semantic content) inputs. Using CNNs for visual features, RNNs for temporal patterns, and transformers for attention-based fusion, the model processes emotional cues in real time. Experiments on datasets like IEMOCAP and CMU-MOSEI show improved emotion recognition over unimodal models. Simulated HRI user studies reveal higher empathy and engagement with emotionally adaptive responses. This work advances emotionally aware robotic systems for more natural and socially effective interactions.