人工智能时代机器人情绪加工的机遇与挑战
Opportunities and Challenges of Emotional Processing for Robots in the Era of Artificial Intelligence
DOI: 10.12677/ap.2026.162064, PDF,    科研立项经费支持
作者: 李嘉润, 申权威*：湖北医药学院应用心理学系，湖北 十堰
关键词: 人工智能；情感计算；情绪加工；应用场景；Artificial Intelligence； Affective Computing； Emotion Processing； Application Scenarios
摘要: 当前，人工智能的发展重心正逐渐由符号计算转向情感计算。在这一背景下，机器人情绪加工已成为人机交互领域的关键研究方向。本文从心理学与计算科学的双重视角出发，分析了机器人情绪加工过程中(注意、识别、评估与响应)的计算机制，描述了机器人情绪加工的应用场景，最后，讨论了情感计算在实现真正情绪理解方面的局限与挑战。未来研究需在技术与伦理之间构建平衡，推动人机情感交互向更具人文关怀的方向发展，在善用科技与科技向善道路上行稳致远。
Abstract: At present, the development focus of artificial intelligence is gradually shifting from symbolic computation to affective computing. Against this backdrop, the processing of robot emotions has become a key research direction in the field of human-computer interaction. This paper, from the dual perspectives of psychology and computational science, analyzes the computational mechanisms in the process of robot emotion processing (attention, recognition, evaluation, and response), describes the application scenarios of robot emotion processing, and finally discusses the limitations and challenges of affective computing in achieving true emotional understanding. Future research needs to strike a balance between technology and ethics, promoting the development of human-computer emotional interaction in a more humanistic direction, and steadily advancing on the path of making good use of technology and making technology beneficial to humanity.
文章引用：李嘉润, 申权威 (2026). 人工智能时代机器人情绪加工的机遇与挑战. 心理学进展, 16(2), 84-94. https://doi.org/10.12677/ap.2026.162064

参考文献

[1] 常亮, 邓小明, 周明全, 武仲科, 袁野, 杨硕, 王宏安(2016). 图像理解中的卷积神经网络. 自动化学报, 42(9), 1300-1312.
[2] 甘怡群(2025). 情感人工智能对社会支持感知的双刃剑效应: 补偿与疏离. 人民论坛∙学术前沿, (20), 48-56.
[3] 国家互联网信息办公室(2025). 专家解读. 智能有度服务有温, 把好“三关”规范AI拟人化互动边界. 国家互联网信息办公室.
[4] 洪延青(2025). 人机“情感交互”的规范化: 指标、机制与多方协同治理. 法治日报, p. 3.
[5] 李欢, 曾烁(2022). 诊断、评估与干预: 近五年卷积神经网络在特殊教育中的实证研究述评. 中国特殊教育, (7), 10-22. .
[6] 李长庭, 赵印, 毕惜茜(2024). 多模态智能审讯技术的原理与实战化应用路径研究. 中国人民公安大学学报(社会科学版), 40(2), 22-29.
[7] 秦兵(2025). 情感交互的层级演进与对齐技术. 见 中国计算机学会(主编), CNCC 2025技术论坛报告集(pp. 1-15).
[8] 王方家(2025). 探索人与智能体共生的心理密码——评《人智交互心理学》. 心理与行为研究, 23(3), 430-432.
[9] 吴静, 董屹泽(2025). 从人工智能情绪识别到情感资本主义: 情感可计算化的哲学反思. 南京社会科学, (7), 94-103.
[10] 姚鸿勋, 邓伟洪, 刘洪海, 洪晓鹏, 王甦菁, 杨巨峰, 赵思成(2022). 情感计算与理解研究发展概述. 中国图象图形学报, 27(6), 6-36.
[11] 中国计算机学会(2025). 《从识别到生成: 智能交互离真情实感还有多远?》论坛举办[摘要]. 技术论坛报告. 中国计算机学会.
[12] Benitti, F. B. V., & Spolaôr, N. (2017). How Have Robots Supported STEM Teaching? In M. S. Khine (Ed.), Robotics in STEM Education: Redesigning the Learning Experience (pp. 103-129). Springer International Publishing.[CrossRef] 
[13] Calvo, R. A., Peters, D., & Vider, K. (2020). The Ethics of Affective Computing: Principles, Challenges, and Opportunities. Foundations and Trends in Human-Computer Interaction, 13, 1-16.
[14] Chen, Y., & Joo, J. (2021). Understanding and Mitigating Annotation Bias in Facial Expression Recognition. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 14960-14971). IEEE.[CrossRef] 
[15] Dupré, D., Krumhuber, E. G., Küster, D., & McKeown, G. J. (2020). A Performance Comparison of Eight Commercially Available Automatic Classifiers for Facial Affect Recognition. PLOS ONE, 15, e0231968.[CrossRef] [PubMed]
[16] Fernandez, J. D., Martinez, F., & Garcia, A. (2023). Energy Consumption Analysis of Multimodal versus Unimodal Affective Models on Edge Devices. Sustainable Computing: Informatics and Systems, 38, Article ID: 100877.
[17] Gross, J. J. (1988). Emotion and Emotion Regulation. In L. A. Pervin, & O. P. John (Eds.), Handbook of Personality: Theory and Research (2nd ed., pp. 525-552). Guilford Press.
[18] Guo, M.-H., Xu, T.-X., Liu, J.-J., Liu, Z.-N., Jiang, P.-T., Mu, T.-J. et al. (2022). Attention Mechanisms in Computer Vision: A Survey. Computational Visual Media, 8, 331-368.[CrossRef] 
[19] IBM Security (2023). Cost of a Data Breach Report 2023. IBM Corporation.
[20] Jack, R. E., Blais, C., Scheepers, C., Schyns, P. G., & Caldara, R. (2009). Cultural Confusions Show That Facial Expressions Are Not Universal. Current Biology, 19, 1543-1548.[CrossRef] [PubMed]
[21] Kohler, M., Gieselmann, H., & Sodian, B. (2023). Social Robots in Kindergarten: Effects on Peer Interaction and Prosocial Behavior. Computers & Education, 196, Article ID: 104727.
[22] Kollias, D., & Zafeiriou, S. (2017). Continuous Regression of Dimensional Emotion for Video. In 2017 12th International Conference on Automatic Face & Gesture Recognition (FG 2017) (pp. 42-49). IEEE Computer Society.
[23] Kouroupa, A., Laws, K. R., Irvine, K., Mengoni, S. E., Baird, A., & Sharma, S. (2022). The Use of Social Robots with Children and Young People on the Autism Spectrum: A Systematic Review and Meta-Analysis. PLOS ONE, 17, e0269800.[CrossRef] [PubMed]
[24] Li, X., Hu, X., & Fu, S. (2022). Cultural Modulation of Neural Responses to Emotional Faces: An EEG Study. NeuroImage, 264, Article ID: 119705.
[25] Lin, V., Yeh, H.-C., & Chen, N.-S. (2022). A Systematic Review on Oral Interactions in Robot-Assisted Language Learning. Electronics, 11, Article No. 290.[CrossRef] 
[26] Lutz, C., & Tamo-Larrieux, A. (2022). The Ethics of Emotional AI and Its Impact on Human Autonomy. Minds and Machines, 32, 667-688.
[27] McQuiggan, S. W., & Lester, J. C. (2007). Modeling and Expressing Empathy in an Intelligent Tutoring System. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS) (pp. 1-8). ACM.
[28] Minaee, S., Minaei, M., & Abdolrashidi, A. (2021). Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network. Sensors, 21, Article No. 3046.[CrossRef] [PubMed]
[29] Nagel, T. (1974). What Is It like to Be a Bat? The Philosophical Review, 83, 435-450.[CrossRef] 
[30] Noroozi, F., Marjanovic, M., Njegus, A., Escalera, S., & Anbarjafari, G. (2017). Challenges and Opportunities in Deep Learning for Automated Emotion Recognition. Pattern Recognition Letters, 99, 86-93.
[31] Ortony, A., Clore, G. L., & Collins, A. (1988). The Cognitive Structure of Emotions. Cambridge University Press.[CrossRef] 
[32] Park, G., & Ko, B. (2023). Beyond Accuracy: Towards Causal Reasoning in Affective Computing. Nature Machine Intelligence, 5, 789-799.
[33] Pessoa, L. (2009). How Do Emotion and Motivation Direct Executive Control? Trends in Cognitive Sciences, 13, 160-166.[CrossRef] [PubMed]
[34] Picard, R. W. (1997). Affective Computing. The MIT Press.[CrossRef] 
[35] Picard, R. W., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D. et al. (2004). Affective Learning—A Manifesto. BT Technology Journal, 22, 253-269.[CrossRef] 
[36] Porter, S., & ten Brinke, L. (2008). Reading between the Lies. Psychological Science, 19, 508-514.[CrossRef] [PubMed]
[37] Pu, L., Moyle, W., Jones, C., & Todorovic, M. (2021). The Effectiveness of Social Robots for Older Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. The Gerontologist, 61, e1-e17.
[38] Rachman, S. (1980). Emotional Processing. Behaviour Research and Therapy, 18, 51-60.[CrossRef] [PubMed]
[39] Santoni de Sio, F., & Mecacci, G. (2021). The Attribution Problem in Emotional AI: Who Is Responsible When Things Go Wrong? Philosophy & Technology, 34, 1587-1611.
[40] Sharkey, A., & Sharkey, N. (2020). We Need to Talk about Deception in Social Robotics! Ethics and Information Technology, 23, 309-316.[CrossRef] 
[41] Sharkey, A., & Sharkey, N. (2021). Who Will Care for the People? The Looming Crisis in Aged Care and the Threat of the Care Robot. AI & Society. Springer Nature
[42] Sharma, A., Lin, Z., & Li, L. (2023). A Computational Approach to Empathetic Responding Using Large Language Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 12074-12089). Association for Computational Linguistics..
[43] Strubell, E., Ganesh, A., & McCallum, A. (2022). The Computational and Energy Cost of Deep Learning and Affective Computing Models. Communications of the ACM, 65, 70-79.
[44] Sullivan, A., & Bers, M. U. (2018). Dancing Robots: Integrating Art, Music, and Robotics in Singapore’s Early Childhood Centers. International Journal of Technology and Design Education, 28, 325-346.[CrossRef] 
[45] Torous, J., Bucci, S., Bell, I. H., Kessing, L. V., Faurholt‐Jepsen, M., Whelan, P. et al. (2021). The Growing Field of Digital Psychiatry: Current Evidence and the Future of Apps, Social Media, Chatbots, and Virtual Reality. World Psychiatry, 20, 318-335.[CrossRef] [PubMed]
[46] Tzirakis, P., Zhang, J., & Schuller, B. W. (2017). End-to-End Multimodal Emotion Recognition Using Deep Neural Networks. Journal of Selected Topics in Signal Processing, 11, 1301-1309.
[47] United Nations General Assembly (2020). United Nations Decade of Healthy Ageing (2021-2030) (Resolution A/RES/75/131).
https://undocs.org/A/RES/75/131
[48] Wang, J., Li, L., & Wang, D. (2022). Multimodal Emotion Recognition: A Systematic Review of Recent Advances and Challenges. Knowledge-Based Systems, 258, Article ID: 110010.
[49] Whitehill, J., Serpell, Z., Lin, Y., Foster, A., & Movellan, J. R. (2014). The Faces of Engagement: Automatic Recognition of Student Engagement from Facial Expressions. IEEE Transactions on Affective Computing, 5, 86-98.[CrossRef] 
[50] Xu, P., & Li, Y. (2023). Multimodal Sentiment Analysis: A Systematic Review of History, Datasets, Multimodal Fusion Methods, Applications, Challenges and Future Directions. Information Fusion, 98, Article ID: 101819.
[51] Xu, T., White, J., Kalkan, S., & Gunes, H. (2020). Investigating Bias and Fairness in Facial Expression Recognition. In A. Vedaldi, H. Bischof, T. Brox, & J.-M. Frahm (Eds.), Computer Vision—ECCV 2020 (pp. 506-523). Springer.[CrossRef] 
[52] Zhan, Y., & Liu, R. (2024). Social Reasoning in Large Language Models: Current State and Future Directions. Proceedings of the National Academy of Sciences, 121, e2401743121.
[53] Zhang, L., & Williams, A. C. (2022). The Privacy Paradox in Personalized Affective Systems: A Framework for Mitigation. Proceedings of the ACM on Human-Computer Interaction, 6, 1-28.
[54] Zhao, D., Sun, X., Shan, B., Yang, Z., Yang, J., Liu, H. et al. (2023a). Research Status of Elderly-Care Robots and Safe Human-Robot Interaction Methods. Frontiers in Neuroscience, 17, Article ID: 1291682.[CrossRef] [PubMed]
[55] Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Wen, J. R. (2023b). A Survey of Large Language Models.
[56] Zhong, B., & Xia, L. (2020). A Systematic Review on Exploring the Potential of Educational Robotics in Mathematics Education. International Journal of Science and Mathematics Education, 18, 79-101.[CrossRef]