Through the Lens of OMRAEG: A Critical Review and Optimizing Roadmap for Robotic Emotion Generation

In the era of human-robot symbiosis, endowing robots with emotional intelligence is essential for creating harmonious human-robot interactions and enabling them to fulfill social functions effectively. Although significant progress has been made in robotic emotion recognition, emotion generation remains underdeveloped, struggling to meet user-centered interaction demands in multi-turn, complex-task, and diverse-scenario settings. This often results in robotic behaviors that appear rigid and lack empathy. To address these challenges, this study proposes an Optimization Model for Robotic Artificial Emotion Generation (OMRAEG). Structured around a “theory–mechanism–method” framework, the model establishes a closed-loop optimization system encompassing method application, effectiveness evaluation, influencing factors, and feedback iteration. Specifically, the research integrates mainstream approaches and key technologies across four dimensions: facial expression synthesis, emotional dialogue generation, emotional speech synthesis, and emotional motion synthesis. Furthermore, it constructs a systematic evaluation indicator system and clarifies the fine-tuning role of application scenarios and development trends guiding technological evolution.