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Learning Proxemics for Personalized Human–Robot Social Interaction

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International Journal of Social Robotics Aims and scope Submit manuscript

Abstract

Each person has their personal area which they do not want to share with others during social interactions. The size of this area usually depends on various factors such as their culture, personal traits, and acquaintanceship. The same applies to the case of human–robot interaction, especially when the robot is required to exhibit a certain level of social competence. Here, we propose a new robot navigation strategy to socially interact with people reflecting upon the social relationship between the robot and each person. To this end, we need a clear definition of interaction areas: (1) quality interaction area where people can be engaged in high-quality interactions with robots, and (2) private area not to be interfered with by the robot speech or action. A technical challenge in enhancing social human–robot interactions is how to enable robots to delineate the boundary of the two areas of each person. Specifically, the social force model (SFM) is designed by a fuzzy inference system, where the membership functions are optimized to give the robot the ability to navigate autonomously in the quality interaction area using a reinforcement learning algorithm. Finally, the proposed model was verified through simulations and experiments with a real robot that can generate a suitable SFM of each person, allowing the robot to maintain the quality of interaction with each person while keeping their private personal distance.

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References

  1. Butler JT, Agah A (2001) Psychological effects of behavior patterns of a mobile personal robot. Auton Robot 10(2):185–202

    Article  Google Scholar 

  2. Edward H (1969) The hidden dimension: man’s use of space in public and in private. The Bodley Head Ltd, London

    Google Scholar 

  3. Pacchierotti E, Christensen HI, J P (2006) Embodied social interaction for service robots in hallway environments. Springer, Berlin

    Book  Google Scholar 

  4. Gockley R, Forlizzi J, Simmons R (2007) Natural person-following behavior for social robots. In: 2007 2nd ACM/IEEE international conference on human–robot interaction (HRI), pp 17–24

  5. Hansen ST, Svenstrup M, Andersen HJ, Bak T (2009) Adaptive human aware navigation based on motion pattern analysis. In: RO-MAN 2009—the 18th IEEE international symposium on robot and human interactive communication, Toyama, Japan, pp 927–932

  6. Jaillet L, Cortes J, Simeon T (2008) Transition-based rrt for path planning in continuous cost spaces. In: 2008 IEEE/RSJ international conference on intelligent robots and systems, pp 2145–2150

  7. Kessler J, Schroeter C, Gross HM (2011) Approaching a person in a socially acceptable manner using a fast marching planner. Springer, Berlin

    Book  Google Scholar 

  8. Kim Y, Mutlu B (2014) How social distance shapes human–robot interaction. Int J Hum Comput Stud 72(12):783–795

    Article  Google Scholar 

  9. Kirby R, Simmons R, Forlizzi J (2009) Companion: a constraint-optimizing method for person-acceptable navigation. In: The proceedings of the IEEE international symposium on robot and human interactive communication

  10. Lam CP, Chou CT, Chiang KH, Fu LC (2011) Human-centered robot navigation; towards a harmoniously human; robot coexisting environment. IEEE Trans Robot 27(1):99–112

    Article  Google Scholar 

  11. Lindner F (2015) A conceptual model of personal space for human-aware robot activity placement. In: 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS), Hamburg, pp 5770–5775

  12. Lu DV, Smart WD (2013) Towards more efficient navigation for robots and humans. In: 2013 IEEE/RSJ international conference on intelligent robots and systems, pp 1707–1713

  13. Luber M, Spinello L, Silva J, Arras KO (2012) Socially-aware robot navigation: a learning approach. In: 2012 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 902–907

  14. Mahadevan S (1996) Average reward reinforcement learning: foundations, algorithms, and empirical results. Mach Learn 22(1):159–195

    MATH  Google Scholar 

  15. Mead R (2012) Space, speech, and gesture in human-robot interaction. In: Proceedings of the 14th ACM international conference on multimodal interaction, New York, USA, pp 333–336

  16. Walters ML, Dautenhahn K, RB (2009) An empirical framework for human-robot proxemics. In: Proceedings of new frontiers in human–robot interaction symposium at the AISB09 convention, pp 144–149

  17. Nakauchi Y, Simmons R (2000) A social robot that stands in line. In: Proceedings of 2000 IEEE/RSJ international conference on intelligent robots and systems (IROS 2000), vol 1, Takamatsu, Japan, pp 357–364

  18. Newell PB (1998) A cross-cultural comparison of privacy definitions and functions: a systems approach. J Environ Psychol 18(4):357–371

    Article  Google Scholar 

  19. Pandey AK, Alami R (2010) A framework towards a socially aware mobile robot motion in human-centered dynamic environment. In: 2010 IEEE/RSJ international conference on intelligent robots and systems, Taipei, pp 5855–5860

  20. Papadakis P, Rives P, Spalanzani A (2014) Adaptive spacing in human-robot interactions. In: 2014 IEEE/RSJ international conference on intelligent robots and systems, Chicago, USA, pp 2627–2632

  21. Patompak P, Jeong S, Chong NY, Nilkhamhang I (2016) Mobile robot navigation for human–robot social interaction. In: 2016 16th International conference on control. automation and systems (ICCAS), Gyeongju, Korea, pp 1298–1303

  22. Patompak P, Jeong S, Chong NY, Nilkhamhang I (2017) Learning social relations for culture aware interaction. In: 14th Ubiquitous robots and ambient intelligence (URAI), Jeju, Korea, pp 1298–1303

  23. Rios-Martinez J (2013) Socially-aware robot navigation: combining risk assessment and social conventions. Dissertation INRIA Sophia-Antipolis, France

  24. Rueben M, Smart WD (2016) Privacy in human–robot interaction survey and future work. In: We robot 2016: the fifth annual conference on legal and policy issues relating to robotics, University of Miami School of Law

  25. Sisbot EA, Marin-Urias LF, Alami R, Simeon T (2007) A human aware mobile robot motion planner. IEEE Trans Robot 23(5):874–883

    Article  Google Scholar 

  26. Svenstrup M, Bak T, Andersen HJ (2010) Trajectory planning for robots in dynamic human environments. In: 2010 IEEE/RSJ international conference on intelligent robots and systems, Taipei, pp 4293–4298

  27. Takayama L, Pantofaru C (2009) Influences on proxemic behaviors in human–robot interaction. In: International conference on IEEE/RSJ intelligent robots and systems, IROS 2009, pp 5495–5502

  28. Tomari R, Kobayashi Y, Kuno Y (2012) Empirical framework for autonomous wheelchair systems in human-shared environments. In: 2012 IEEE international conference on mechatronics and automation, Chengdu, pp 493–498

  29. Tora E, Cuijpers RH, Juolia JF, Van Der Pol D (2012) Modelling and testing proxemic behavior for humanoid robots. Int J Humanoid Robot 09(04):1250,028

    Article  Google Scholar 

  30. Tutsoy O, Brown M (2016) Reinforcement learning analysis for a minimum time balance problem. Trans Inst Meas Control 38(10):1186–1200

    Article  Google Scholar 

  31. Westin A (1970) Privacy and freedom. Bodley Head, London

    Google Scholar 

  32. Zeeger SK, Readdick CA, Hansen-Gandy S (1994) Daycare children’s establishment of territory to experience privacy. Child Environ 11:265–271

    Google Scholar 

Download references

Acknowledgements

This work was supported by the EU-Japan coordinated R&D project on “Culture Aware Robots and Environmental Sensor Systems for Elderly Support” commissioned by the Ministry of Internal Affairs and Communications of Japan and EC Horizon 2020.

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Author notes

  1. Pakpoom Patompak and Sungmoon Jeong are contributed equally to this work.

Authors and Affiliations

  1. School of Information Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, Japan

    Pakpoom Patompak & Nak Young Chong

  2. Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Phahonyothin Rd., Tambon Khlong Nung, Amphoe Khlong Luang, Pathum Thani, Thailand

    Pakpoom Patompak & Itthisek Nilkhamhang

  3. Center for Artificial Intelligence in Medicine, Bio-medical Research Institute, Kyungpook National University Hospital, Daegu, Republic of Korea

    Sungmoon Jeong

  4. School of Medicine, Kyungpook National University, Daegu, Republic of Korea

    Sungmoon Jeong

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  1. Pakpoom Patompak
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  2. Sungmoon Jeong
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  3. Itthisek Nilkhamhang
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  4. Nak Young Chong
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Correspondence to Pakpoom Patompak.

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Patompak, P., Jeong, S., Nilkhamhang, I. et al. Learning Proxemics for Personalized Human–Robot Social Interaction. Int J of Soc Robotics 12, 267–280 (2020). https://doi.org/10.1007/s12369-019-00560-9

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  • DOI: https://doi.org/10.1007/s12369-019-00560-9

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