Seamless Multimodal Human-Robot Communication: Integration Techniques in Human–Computer Interaction

Summary

This review synthesizes recent advances in deep learning, multimodal sensing, and integration strategies that enable seamless, adaptive, and human-centered communication between humans and robots.

Abstract

Seamless multimodal human-robot communication has become essential as social, assistive, and educational robots move into everyday settings like homes, healthcare facilities, and classrooms, where they need to integrate speech, gesture, gaze, facial expressions, and tactile cues with high precision, low latency, and real-world robustness. This review systematically examines how current techniques achieve real-time, reciprocal multimodal human-robot interaction (HRI), focusing on fusion strategies, system architectures, and applications in specific domains. We searched the Web of Science Core Collection for English-language empirical studies from 2015 to 2025, selecting 148 papers that address communicative integration. The most important insights include a clear shift toward hybrid and attention-based fusion since 2022 (about 43% of approaches), which better handles temporal asynchrony and embodied interactions than earlier methods; widespread inconsistency in evaluation metrics that hinders cross-study comparisons; and a persistent gap between strong laboratory performance and weaker real-world robustness under noise, occlusion, or user variability. Key challenges include real-time processing, semantic alignment, data efficiency, deployment robustness, and the under-explored integration of tactile signals with affect. Looking ahead, the review suggests prioritizing adaptive fusion policies, standardized benchmarks for synchronization and fluency, and continual learning to enable user-personalized adaptation. Ultimately, it aims to guide the development of more human-centered robotic agents that can engage collaboratively, meaningfully, and are socially acceptable in daily life.

Introduction

Robots have evolved from industrial tools in structured settings to socially embedded agents in homes, hospitals, and classrooms. They now assist in education, therapy, and companionship, reflecting a shift from task-oriented automation to user-centered, adaptive interaction. At the core of this transition lies multimodal communication, enabling robots to perceive and respond to human cues—speech, gaze, gestures, facial expressions, and touch—with the temporal precision needed for natural exchanges in dynamic environments. This emphasis on coordination and responsiveness aligns with central concerns in human–computer interaction and cognitive science....