情智兼备数字人与机器人研究进展

The development of emotionally intelligent digital humans and robotic technologies represents a significant advancement in contemporary research,focusing on the creation of systems capable of understanding and responding to human emotions in a nuanced manner. This paper systematically analyzes the current research status and advancements in four key areas:brain-cognition-driven emotional mechanisms,the integration and interpretation of multimodal emotional intelligence models,personalized emotional representation and dynamic computation,and the regulation of interactive emotional content generation. The brain-cognition-driven emotional mechanisms highlight the critical need to understand emotional characteristics and dynamic regulatory processes across various brain regions. Recent advances in neuroimaging technologies,such as functional magnetic resonance imaging and electroencephalography,have yielded deeper insights into the activation patterns of these areas in response to different emotional stimuli. For instance,the amygdala’s association with fear responses contrasts with the prefrontal cortex’s role in emotional regulation and cognitive control,emphasizing the importance of identifying these unique functions for the development of accurate emotion recognition systems capable of real-time emotional state analysis. The integration and interpretation of multimodal emotional intelligence models are also essential for enhancing emotion recognition capabilities. The ability to synthesize information from diverse sources,including audio,video,text,and physiological signals,provides a more robust understanding of emotional expressions. By analyzing vocal tone alongside facial expressions and contextual text data,models can achieve superior accuracy in identifying a spectrum of emotions such as happiness,sadness,or anger. This paper delves into methodologies for aligning and fusing cross-modal emotional data,showcasing how techniques,like deep learning and Transformer architectures,address challenges related to differences in modal features and temporal synchronization. For example,ensuring that emotional cues from video and audio data are accurately aligned in time can significantly enhance the overall recognition process. The discussion further explores the application of large models,particularly their capabilities in transfer and self-supervised learning,which enable these systems to adapt to new emotional contexts with minimal additional training. Such adaptability not only improves the naturalness of emotional expressions but also addresses critical privacy concerns associated with processing emotional data. In addition to these foundational elements,the paper emphasizes personalized emotional representation and dynamic computation. By capturing individual emotional traits,such as those related to gender,age,cultural background,and personality type,models can create more accurate emotional profiles tailored to specific users. This individualized approach is particularly relevant in areas such as mental health support,where a nuanced understanding of users’ emotional landscape can significantly enhance intervention effectiveness. The integration of social relationships and environmental stimuli into emotional analysis is also discussed,highlighting how contextual factors influence emotional responses and lead to more appropriate system reactions. Hierarchical knowledge-guided technologies are highlighted as enabling systems to respond to complex emotional scenarios,fostering more nuanced and context-aware interactions. Moreover,adaptive dynamic modeling techniques for emotional states introduce temporal dimensions into emotion processing,allowing real-time adjustments that ensure responses remain relevant and sensitive to user needs. The regulation of interactive emotional content generation is another critical aspect of this review,aiming to develop intelligent systems that can understand and produce multimodal emotional content. Key components include constructing emotional spaces for precise emotion representation,which involves defining discrete categories and continuous dimensions of emotional expression. This dual approach enhances the ability of systems to capture a wide range of emotional nuances,facilitating more accurate and relatable interactions. Furthermore,the paper examines controllable interaction technologies in emotional generation,particularly advancements in generative adversarial networks and diffusion models,which allow for the introduction of emotional conditions that guide content generation. This capability enhances the flexibility and relevance of emotional responses,enabling digital humans to adjust their expressions based on users’emotional states for more engaging interactions. Utilizing multimodal reasoning is a crucial element of the discussion,as it leverages the inferential capabilities of large multimodal models to effectively align and generate cross-modal emotional information. This enriches generated content and ensures resonance with users across visual,auditory,and textual levels. The paper also addresses strategies for minimizing computational resources while maintaining content quality,essential for deploying these advanced systems in real-world applications where efficiency is paramount. In conclusion,emotionally intelligent digital humans signify a transformative advancement in human-computer interaction,with the potential to significantly enhance user engagement and satisfaction. By integrating high-fidelity digital reconstruction,controllable emotional expression,and intelligent interaction capabilities,these systems can facilitate more natural and effective user engagement. Future developments in this field are likely to focus on enhancing the realism of digital human interactions and improving the adaptability of emotional expressions based on user feedback. As technology continues to progress,the potential applications of emotionally intelligent digital humans and robotics will expand across various domains,including healthcare,where they can provide companionship and emotional support;education,where they can personalize learning experiences;and entertainment,where they can create immersive environments. Ultimately,these advancements promise enriched user experiences and deeper emotional connections,paving the way for a future where emotionally intelligent systems become integral to daily life.