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Recent Trends in Human-Care Robot and Social Interaction Technology

휴먼케어 로봇과 소셜 상호작용 기술 동향

  • 고우리 (인간로봇상호작용연구실) ;
  • 조미영 (인간로봇상호작용연구실) ;
  • 김도형 (인간로봇상호작용연구실) ;
  • 장민수 (인간로봇상호작용연구실) ;
  • 이재연 (인간로봇상호작용연구실) ;
  • 김재홍 (인간로봇상호작용연구실)
  • Published : 2020.06.01

Abstract

This paper examines the trends of recently developed human-care robots and social interaction technologies. As one of the solutions to the problems of an aging society, human-care robots have gained considerable attention from the public and the market. However, commercialized human-care robots do not meet user expectations for the role as companions. Current robot services based on short-term interaction and fragmentary pieces of intelligence have encountered difficulty in eliciting natural communication with humans. This results in the failure of human-robot social bonding. Social interaction is being actively investigated as a technique for improving robots' natural communication skills. Robots can form a natural bond with humans through social interaction, which consequently increases their effectiveness. In this paper, we introduce recent human-care robot-related issues and subsequently describe technical challenges and implications for the success of human-care robots. In addition, we review recent trends on social interaction technologies and the datasets required.

Keywords

Acknowledgement

본 연구는 미래창조과학부 및 정보통신기술진흥센터의 정보통신·방송 연구개발 사업의 일환으로 수행하였음[2017-0-00162, 고령 사회에 대응하기 위한 실환경 휴먼케어 로봇 기술 개발].

References

  1. M. De Graaf, S. B. Allouch, and J.van Diik, "Why do they refuse to use my robot?: Reasons for non-use derived from a longterm home study," in Proc. ACM/IEEE Int. Conf. Human-Robot Interaction (HRI), Vienna, Austria, Mar. 2017, doi: 10.1145/2909824.3020236.
  2. G.-Z. Yang et al., "The grand challenges of Science Robotics," Sci. Robotics, vol. 3, no. 14, 2018, doi: 10.1126/scirobotics.aar7650.
  3. 김재홍, 장민수, "고령자케어 서비스 연구동향," 로봇공학회지: 로봇과 인간, 제13권 제4호, 2016, pp. 6-12.
  4. The Partnership for Robotics in Europe, "Robotics 2020 Multi-Annual Roadmap: For Robotics in Europe Horizon 2020 Call ICT-2017(ICT-25, ICT-27, ICT-28)," Feb 12, 2016, pp. 211-212, https://www.eu-robotics.net/cms/upload/topic_groups/H2020_Robotics_Multi-Annual_Roadmap_ICT-2017B.pdf.
  5. G. Hoffman, "Anki, Jibo, and Kuri: What We Can Learn from Social Robots That Didn't Make It," IEEE Spectrum, May, 2019, https://spectrum.ieee.org/automaton/robotics/homerobots/anki-jibo-and-kuri-what-we-can-learn-from-socialrobotics-failures
  6. 장길수, "튜링테스트 오류 발견했다," 로봇신문, 2016. 7. 11.
  7. S. Ohlsson et al., "Measuring an artificial intelligence system's performance on a verbal IQ test for young children," J. Experimental Theoretical Artif. Intell., vol. 29, no. 4, 2017, pp. 679-693, doi: 10.1080/0952813X.2016.1213060.
  8. http://www.robocupathome.org/
  9. http://rockinrobotchallenge.eu/home.php
  10. https://worldrobotsummit.org/en/wrs2020/challenge/
  11. H. M. Huang and E. R. Messina, "Autonomy levels for unmanned systems (ALFUS) framework volume II: framework models initial version," No. Special Publication (NIST SP)-1011-II-1.0, 2007.
  12. SPARC Robotics, "Robotics 2020 multi-annual roadmap for robotics in Europe," SPARC Robotics, EU-Robotics AISBL, The Hauge, The Netherlands, accessed Feb 5, 2018.
  13. https://www.innovativeotsolutions.com/
  14. H. Gardner, "Frames of mind: the theory of multiple intelligences," Basic Books, New York, 1983.
  15. A. Zaraki et al., "Toward autonomous child-robot interaction: development of an interactive architecture for the humanoid kaspar robot," in Proc. Workshop Child-Robot Interaction HRI, Vienna, Austria, Mar. 6, 2017, http://orca.cf.ac.uk/id/eprint/129029.
  16. A. Aly and A. Tapus, "Prosody-based adaptive metaphoric head and arm gestures synthesis in human robot interaction," in Proc. Int. Conf. Adv. Robot. (ICAR), Montevideo, Uruguay, Nov. 2013, doi: 10.1109/ICAR.2013.6766507.
  17. S.F.R. Alves, M. Shao, and G. Nejat, "A socially assistive robot to facilitate and assess exercise goals," in Proc. Int. Conf. Robot. Autom, Montreal, Canada, May 2019, https://morobae.github.io/papers/morobae_p10_alves.pdf.
  18. K. Matsumura, T. Gompei, and Y. Sumi, "Robot behavior designed to encourage conversations between visitors in an exhibition space," in Proc. IEEE Int. Symp. Robot Human Interactive Commun., Edinburgh, UK, Aug. 2014, doi: 10.1109/ROMAN.2014.6926269.
  19. A. H. Qureshi et al., "Robot gains social intelligence through multimodal deep reinforcement learning," in Proc. IEEE-RAS Int. Conf. Humanoid Robots (Humanoids), Cancun, Mexico, Nov. 2016, doi: 10.1109/HUMANOIDS.2016.7803357.
  20. P. Liu et al., "Learning proactive behavior for interactive social robots," Autonomous Robots, vol. 42, 2018, pp. 1067-1085, doi: 10.1007/s10514-017-9671-8.
  21. T. Kucherenko, "Data driven non-verbal behavior generation for humanoid robots," in Proc. ACM Int. Conf. Multimodal Interaction, Boulder, CO, USA, Oct. 2018, pp. 520-523, doi: 10.1145/3242969.3264970.
  22. Y. Yoon et al., "Robots learn social skills: End-to-end learning of co-speech gesture generation for humanoid robots," in Proc. Int. Conf. Robot. Autom. (ICRA), Montreal, Canada, May 2019, doi: 10.1109/ICRA.2019.8793720.
  23. W.-R. Ko et al., "End-to-End Learning-based Interaction Behavior Generation for Social Robots," in Proc. Int. Conf. Social Robotics (ICSR), Qingdao, China, 2018.
  24. Artificial Intelligence for Robots, "Datasets," https://ai4robot.github.io/datasets/
  25. T. Kim and J.-H. Lee, "C-3PO: Cyclic-Three-Phase Optimization for human-robot motion retargeting based on reinforcement learning," arXiv:1909.11303, 2019.
  26. M. S. Ryoo et al., "Robot-centric activity prediction from firstperson videos: What will they do to me?" in Proc. ACM/IEEE Int. Conf. Human-Robot Interaction (HRI), Portland, OR, USA, Mar. 2015, doi: 10.1145/2696454.2696462.
  27. O. Celiktutan, E. Skordos, and H. Gunes, "Multimodal human- human-robot interactions (MHHRI) dataset for studying personality and engagement," IEEE Trans. Affective Comput., vol. 10, no. 4, 2019, pp. 484-497, doi: 10.1109/TAFFC.2017.2737019.
  28. K. Bagewadi, J. Campbell, and H.B. Amor, "Multimodal dataset of human-robot hugging interaction," arXiv:1909.07471, 2019.
  29. J. Liu et al., "NTU RGB+D 120: A large-scale benchmark for 3d human activity understanding," IEEE Trans. Pattern Anal. Mach. Intell., Early Access, 2019, doi: 10.1109/TPAMI.2019.2916873.