Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
80 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Survey of Design Paradigms for Social Robots (2407.20556v1)

Published 30 Jul 2024 in cs.RO, cs.CL, and cs.CY

Abstract: The demand for social robots in fields like healthcare, education, and entertainment increases due to their emotional adaptation features. These robots leverage multimodal communication, incorporating speech, facial expressions, and gestures to enhance user engagement and emotional support. The understanding of design paradigms of social robots is obstructed by the complexity of the system and the necessity to tune it to a specific task. This article provides a structured review of social robot design paradigms, categorizing them into cognitive architectures, role design models, linguistic models, communication flow, activity system models, and integrated design models. By breaking down the articles on social robot design and application based on these paradigms, we highlight the strengths and areas for improvement in current approaches. We further propose our original integrated design model that combines the most important aspects of the design of social robots. Our approach shows the importance of integrating operational, communicational, and emotional dimensions to create more adaptive and empathetic interactions between robots and humans.

PDF Abstract
Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (62)
  1. A multimodal human-robot interaction manager for assistive robots. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 6756–6762.
  2. Augmenting knowledge through statistical, goal-oriented human-robot dialog. IEEE International Conference on Intelligent Robots and Systems, pages 744–750.
  3. Development of a new set of heuristics for the evaluation of human-robot interaction in industrial settings: Heuristics robots experience (heurobox). Frontiers in Robotics and AI, 10.
  4. Virtual reality for supporting the treatment of depression and anxiety: Scoping review. JMIR mental health, 8(9):e29681.
  5. Bartneck, C. (2023). Godspeed Questionnaire Series: Translations and Usage, pages 1–35.
  6. More than words: A framework for describing human-robot dialog designs. In Proceedings of the 2022 ACM/IEEE International Conference on Human-Robot Interaction, HRI ’22, page 393–401. IEEE Press.
  7. Acceptance and usability of a relational agent interface by urban older adults. In CHI ’05 Extended Abstracts on Human Factors in Computing Systems, CHI EA ’05, page 1212–1215, New York, NY, USA. Association for Computing Machinery.
  8. A review of lower extremity assistive robotic exoskeletons in rehabilitation therapy. Critical Reviews™ in Biomedical Engineering, 41(4-5).
  9. Dautenhahn, K. (2007). Socially intelligent robots: Dimensions of human–robot interaction. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1480):679–704.
  10. Survey on evaluation methods for dialogue systems. Artificial Intelligence Review, 54:755–810.
  11. Rmes: Real-time micro-expression spotting using phase from riesz pyramid. arXiv preprint arXiv:2305.05523.
  12. Modelling multimodal dialogues for social robots using communicative acts. Sensors (Basel, Switzerland), 20.
  13. A survey of socially interactive robots. Robotics and Autonomous Systems, 42(3-4):143–166.
  14. The mummer project: Engaging human-robot interaction in real-world public spaces. In Social Robotics: 8th International Conference, ICSR 2016, Kansas City, MO, USA, November 1-3, 2016 Proceedings 8, pages 753–763. Springer.
  15. Erit lightweight multimodal dataset for elderly emotion recognition and multimodal fusion evaluation. arXiv preprint arXiv:2407.17772.
  16. Hallucinations in neural automatic speech recognition: Identifying errors and hallucinatory models.
  17. Communication models in human–robot interaction: An asymmetric model of alterity in human–robot interaction (amodal-hri). International Journal of Social Robotics, 15(3):473–500.
  18. Multimodal dialogue systems via capturing context-aware dependencies of semantic elements. In Proceedings of the 28th ACM International Conference on Multimedia, MM ’20, page 2755–2764, New York, NY, USA. Association for Computing Machinery.
  19. ERICA: An empathetic android companion for covid-19 quarantine. In Li, H., Levow, G.-A., Yu, Z., Gupta, C., Sisman, B., Cai, S., Vandyke, D., Dethlefs, N., Wu, Y., and Li, J. J., editors, Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 257–260, Singapore and Online. Association for Computational Linguistics.
  20. Design of an entertainment robot with multimodal human-robot interactions. In 2008 SICE Annual Conference, pages 1437–1441. IEEE.
  21. Survey of hallucination in natural language generation. ACM Comput. Surv., 55(12).
  22. Exploring the entertainment value of playing games with a humanoid robot. International Journal of Social Robotics, 8:247–269.
  23. Jokinen, K. (2018). Dialogue models for socially intelligent robots. undefined.
  24. Human-robot dialogues for explaining activities. In International Workshop on Spoken Dialogue Systems Technology.
  25. A visually-grounded first-person dialogue dataset with verbal and non-verbal responses. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3299–3310, Online. Association for Computational Linguistics.
  26. Laird, J. E. (2012). The Soar Cognitive Architecture. The MIT Press.
  27. A standard model of the mind: Toward a common computational framework across artificial intelligence, cognitive science, neuroscience, and robotics. Ai Magazine, 38(4):13–26.
  28. Findings from human-android dialogue research with erica. 1st RobotDial Workshop on Dialogue Models for Human-Robot Interaction.
  29. Leontiev, A. N. (1978). Activity, consciousness, and personality.
  30. A syntactically constrained bidirectional-asynchronous approach for emotional conversation generation. arXiv preprint arXiv:1806.07000.
  31. A robot-based digital storytelling approach to enhancing efl learners’ multimodal storytelling ability and narrative engagement. Computers & Education, 201:104827.
  32. Moel: Mixture of empathetic listeners. arXiv preprint arXiv:1908.07687.
  33. Knowledge diffusion for neural dialogue generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1489–1498.
  34. A multimodal emotional communication based humans-robots interaction system. In 2016 35th Chinese Control Conference (CCC), pages 6363–6368. IEEE.
  35. Majgaard, G. (2015). Multimodal robots as educational tools in primary and lower secondary education. In I The 9th International Conference on Interfaces and Human Computer Interaction, volume 22, page 24.
  36. Modeling grounding for interactive social companions. KI - Kunstliche Intelligenz, 30:45–52.
  37. A conversational dialogue manager for the humanoid robot erica. In International Workshop on Spoken Dialogue Systems Technology.
  38. Towards exploiting background knowledge for building conversation systems. arXiv preprint arXiv:1809.08205.
  39. Recent advances in deep learning based dialogue systems: A systematic survey. Artif. Intell. Rev., 56(4):3055–3155.
  40. OpenAI (2023). Gpt-4 technical report.
  41. A survey on dialogue management in human-robot interaction. arXiv preprint arXiv:2307.10897.
  42. Act‐r: A cognitive architecture for modeling cognition. WIREs Cognitive Science, 10(3).
  43. Generating personalized dialogue towards daily counseling system for home dementia care. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10287 LNCS:161–172.
  44. Scholtz, J. (2003). Theory and evaluation of human robot interactions. In 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the, pages 10 pp.–.
  45. A review on lower limb rehabilitation exoskeleton robots. Chinese Journal of Mechanical Engineering, 32(1):1–11.
  46. Robot therapy: a new approach for mental healthcare of the elderly - a mini-review. Gerontology, 57:378–386.
  47. An evaluation of human conversational preferences in social human-robot interaction. Applied Bionics and Biomechanics, 2021:1–13.
  48. Recent advancements in multimodal human–robot interaction. Frontiers in Neurorobotics, 17.
  49. Multimodal dialogue response generation. In Muresan, S., Nakov, P., and Villavicencio, A., editors, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2854–2866, Dublin, Ireland. Association for Computational Linguistics.
  50. Expecting, understanding, relating, and interacting-older, middle-aged and younger adults’ perspectives on breakdown situations in human–robot dialogues. Frontiers in Robotics and AI, 9.
  51. Learning to abstract for memory-augmented conversational response generation. In Proceedings of the 57th annual meeting of the Association for Computational Linguistics, pages 3816–3825.
  52. Teachers’ perspectives on social robots in education: an exploratory case study. In Proceedings of the interaction design and children conference, pages 273–280.
  53. Improving knowledge-aware dialogue generation via knowledge base question answering. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 9169–9176.
  54. Reducing stress and anxiety in the metaverse: A systematic review of meditation, mindfulness and virtual reality. In Proceedings of the 10th International Symposium of Chinese CHI (Chinese CHI 2022), Guangzhou, China.
  55. Designing loving-kindness meditation in virtual reality for long-distance romantic relationships. In Proceedings of the 31st ACM International Conference on Multimedia, pages 7608–7617.
  56. Wright, J. (2023). Robots Won’t Save Japan An Ethnography of Eldercare Automation. Cornell University Press, Ithaca, NY.
  57. Proactive human-machine conversation with explicit conversation goals. arXiv preprint arXiv:1906.05572.
  58. A comprehensive assessment of dialog evaluation metrics. In Wei, W., Dai, B., Zhao, T., Li, L., Yang, D., Chen, Y.-N., Boureau, Y.-L., Celikyilmaz, A., Geramifard, A., Ahuja, A., and Jiang, H., editors, The First Workshop on Evaluations and Assessments of Neural Conversation Systems, pages 15–33, Online. Association for Computational Linguistics.
  59. Personalizing dialogue agents: I have a dog, do you have pets too? arXiv preprint arXiv:1801.07243.
  60. Task-oriented dialog systems that consider multiple appropriate responses under the same context. Proceedings of the AAAI Conference on Artificial Intelligence, 34:9604–9611.
  61. Unsupervised context rewriting for open domain conversation. arXiv preprint arXiv:1910.08282.
  62. The trusted listener: The influence of anthropomorphic eye design of social robots on user’s perception of trustworthiness. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, CHI ’22, New York, NY, USA. Association for Computing Machinery.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (5)
  1. Rita Frieske (11 papers)
  2. Xiaoyu Mo (15 papers)
  3. Yini Fang (9 papers)
  4. Jay Nieles (1 paper)
  5. Bertram E. Shi (28 papers)