As robots increasingly enter homes, hospitals, factories, classrooms, and public environments, natural and trustworthy communication between humans and autonomous agents becomes essential. Single-modality interfaces are no longer sufficient. Effective human-robot interaction requires the seamless integration of speech, vision, gesture, motion, probabilistic reasoning, and ethical decision-making.

Multimodal Communication in Human-Robot Interaction presents a comprehensive and systems-level framework for designing interactive robots capable of interpreting and generating coordinated multimodal behavior. Rather than treating speech processing, computer vision, motion planning, and artificial intelligence as isolated components, this textbook proposes a path forward on how they operate together within a perception-action cycle that enables robots to understand human intent, act safely in shared environments, and communicate their reasoning transparently.

Foundational and advanced topics are integrated, including spoken dialogue systems, wake-word detection, speaker verification, object recognition, gesture interpretation, audiovisual synchronization, probabilistic state estimation, cooperative control, socially aware navigation, and explainable artificial intelligence. A contribution of the book is its emphasis on multimodal robots and generative AI that not only interpret human signals but also produce aligned speech, gesture, motion, and explanation in context-sensitive and safety-aware ways.

Ethical considerations are treated as essential for the robotic design and interaction process. As robotic systems gain autonomy through deep learning and large generative models, the ethical considerations become even more relevant. This textbook examines the topics of training bias, explainability, accountability, transparency, and the carbon footprint created by AI systems. In addition, questions regarding consciousness, sapience, and superintelligence are also discussed.