Article published in:
Social Cues in Robot Interaction, Trust and Acceptance
Edited by Alessandra Rossi, Kheng Lee Koay, Silvia Moros, Patrick Holthaus and Marcus Scheunemann
[Interaction Studies 20:3] 2019
► pp. 530–560

Reshaping human intention in Human-Robot Interactions by robot moves

A comparative analysis of HMM and OOM methods

| Konya Technical University
| Middle East Technical University
| The Ohio State University
This paper outlines the methodology and experiments associated with the reshaping of human intentions based on robot movements within Human-Robot Interactions (HRIs). Although studies on estimating human intentions are well studied in the literature, reshaping intentions through robot-initiated interactions is a new significant branching in the field of HRI. In this paper, we analyze how estimated human intentions can intentionally change through cooperation with mobile robots in real Human-Robot environments. This paper proposes an intention-reshaping system that includes either the Observable Operator Models (OOMs) or Hidden Markov Models (HMMs) to estimate human intention and decide which moves a robot should perform to reshape previously estimated human intentions into desired ones. At the low level, the system needs to track the locations of all mobile agents using cameras. We test our system on videos taken in a real HRI environment that has been developed as our experimental setup. The results show that OOMs are faster than HMMs and both models give correct decisions for testing sequences.
Keywords: Human-Robot Interaction, Hidden Markov Models, Observable Operator Models, intention reshaping, intention recognition
Published online: 18 November 2019
https://doi.org/10.1075/is.18068.dur
References

References

Aarno, D., & Kragic, D.
(2006) Layered HMM for motion intention recognition, In IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing 2006, pp. 5130–5135.   CrossrefGoogle Scholar
Arasaratnam, I., Haykin, S., Kirubarajan, T., & Dilkes, F.
(2006) Tracking the mode of operation of multi-function radars. 2006 IEEE Conference on Radar, Verona, NY, USA. CrossrefGoogle Scholar
Bratman, M.
(1999) Intention, plans, and practical reason. Harvard University Press. CrossrefGoogle Scholar
Baum, L. E., Petrie, T., Soules, G., & Weiss, N.
(1970) A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Ann. Math. Statist., 41, 1, pp. 164–171. CrossrefGoogle Scholar
Charniak, E., & Goldman, R.
(1993) A Bayesian model of plan recognition, Artif. Intell. 64, pp. 53–79. CrossrefGoogle Scholar
Chouchourelou, A., Matsuka, T., Harber, K., & Shiffrar, M.
(2006) The visual analysis of emotional actions. Social Neuroscience, 1, pp. 63–74. CrossrefGoogle Scholar
Clarke, T. J., Bradshaw, M. F., Field, D. T., Hampson, S. E., & Rose, D.
(2005) The perception of emotion from body movement in point-light displays of interpersonal dialogue. Perception, 34, pp. 1171–1180. CrossrefGoogle Scholar
Cutting, J. E., & Kozlowski, L. T.
(1977) Recognizing friends by their walk-gait perception without familiarity cues. Bulletin of the Psychonomic Society, 9, pp. 353–356. CrossrefGoogle Scholar
Daprati, E., Wriessnegger, S., & Lacquaniti, F.
(2007) Kinematic cues and recognition of self-generated actions. Experimental Brain Research, 177, pp. 31–44. CrossrefGoogle Scholar
Dennett, D. C.
(1987) The Intentional Stance. MIT Press. CrossrefGoogle Scholar
Dielmann, A., & Renals, S.
(2004) Dynamic Bayesian Networks for Meeting Structuring. In Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, pp. 629–632. CrossrefGoogle Scholar
[ p. 557 ]
Dittrich, W. H., Troscianko, T., Lea, S. E. G., & Morgan, D.
(1996) Perception of emotion from dynamic point-light displays represented in dance. Perception, 25, 727–738. CrossrefGoogle Scholar
Durdu, A., Erkmen, I., Erkmen, A. M., Yilmaz, A.
(2011) Morphing Estimated Human Intention via Human-Robot Interactions, Lecture Notes in Engineering and Computer Science: Proceedings of The World Congress on Engineering and Computer Science 2011, WCECS 2011, San Francisco, USA, pp. 354–359.Google Scholar
(2012) Chapter 13: Robotic Hardware and Software Integration for Changing Human Intentions. In Prototyping of Robotic Systems: Applications of Design and Implementation, Edited by T. Sobh and X. Xiong, Pennsylvania: IGI Global Publisher 2012 CrossrefGoogle Scholar
Durdu, A., Erkmen, I., Erkmen, A. M.
(2012) Observable Operator Models for Reshaping Estimated Human Intention by Robot Moves in Human-Robot Interactions, IEEE–INISTA-12 International Symposium on Innovations in Intelligent Systems and Applications, July 2012, Trabzon, TURKIYE. CrossrefGoogle Scholar
(2016) Estimating and Reshaping Human Intention via Human-Robot Interaction, Turkish J. Elec Eng & Comp Sci, 24, 1, pp. 88–104.Google Scholar
Grezes, J., Frith, C., & Passingham, R. E.
(2004) Brain mechanisms for inferring deceit in the actions of others. Journal of Neuroscience, 24, pp. 5500–5505. CrossrefGoogle Scholar
Jaeger, H., Zhao, M., and Kolling, A.
(2005) Efficient estimation of OOMs. In Advances in Neural Information Processing Systems (NIPS). MIT Press.Google Scholar
Kelley, R., Nicolescu, M., Tavakkoli, A., Nicolescu, M., Christopher King, George Bebis
(2008) Understanding Human Intentions via Hidden Markov Models in Autonomous Mobile Robots. HRI’08, March 12–15 2008, Amsterdam, Netherlands. CrossrefGoogle Scholar
Knoblich, G., & Prinz, W.
(2001) Recognition of self-generated actions from kinematic displays of drawing. Journal of Experimental Psychology: Human Perception and Performance, 27, pp. 456–465.Google Scholar
Kohler, E., Keysers, C., Umilta, M. A., Fogassi, L., Gallese, V., & Rizzolatti, G.
(2002) Hearing sounds, understanding actions: Action representation in mirror neurons. Science, 297, pp. 846–848. CrossrefGoogle Scholar
Lee, K. K., & Xu, Y.
(2004) Modeling human actions from learning. in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS ’04), 3, pp. 2787–2792.Google Scholar
Loula, F., Prasad, S., Harber, K., & Shiffrar, M.
(2005) Recognizing people from their movement. Journal of Experimental Psychology: Human Perception and Performance, 31, pp. 210–220.Google Scholar
Manera, V., Schouten, B., Becchio, C., Bara, B. G., & Verfaillie, K.
(2010) Inferring intentions from biological motion: A stimulus set of point-light communicative interactions. Behavior Research Methods, 42, pp. 168–178. CrossrefGoogle Scholar
Meltzoff, A. N.
(1995) Understanding the intentions of others: Reenactment of intended acts by 18-month-old children. Developmental Psychology, 31, 5, pp 1–16. CrossrefGoogle Scholar
Miyake, T., Matsumoto, T., Imamura, T., & Zhang, Z. E.
(2011) Estimation of facial expression from its change in time. ICIC Express Letters, Part B: Applications, 2, 3, pp 641–645.Google Scholar
[ p. 558 ]
Nakauchi, Y., Noguchi, K., Somwong, P., Matsubara, T., & Namatame, A.
(2003) Vivid room: human intention detection and activity support environment for ubiquitous autonomy. Intelligent Robots and Systems (IROS 2003).Google Scholar
Noguchi, K., Somwong, P., Matsubara, T., & Nakauchi, Y.
(2003) Human Intention Detection and Activity Support System for Ubiquitous Autonomy. Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation 2003, pp. 906–911. CrossrefGoogle Scholar
Pynadath, D.
(1999) Probabilistic Grammars for Plan Recognition, Doctoral Thesis, the University of Michigan, MI.Google Scholar
Rabiner, L. R.
(1989) A tutorial on Hidden Markov Models and selected applications in speech recognition. Proceedings of the IEEE 77 (2): 257–286. CrossrefGoogle Scholar
Roether, C. L., Omlor, L., Christensen, A., & Giese, M.
(2009) Critical features for the perception of emotion from gait. Journal of Vision, 9, pp. 1–32. CrossrefGoogle Scholar
Runeson, S., & Frykholm, G.
(1981) Visual perception of lifted weight. Journal of Experimental Psychology: Human Perception and Performance, 7, pp.733–740.Google Scholar
Sebanz, N., & Shiffrar, M.
(2009) Detecting deception in a bluffing body: The role of expertise. Psychonomic Bulletin & Review, 16, pp. 170–175. CrossrefGoogle Scholar
Russell, S. J., & Norvig, P.
(2003) Artificial Intelligence: A Modern Approach, Prentice Hall series in artificial intelligence. Prentice Hall, second edition.Google Scholar
Schmidt, S., & Färber, B.
(2009) Pedestrians at the kerb – Recognising the action intentions of humans. Transportation Research Part F, 12, pp. 300–310. CrossrefGoogle Scholar
Schrempf, O. C., Albrecht, D., & Hanebeck, U. D.
(2007) Tractable Probabilistic Models for Intention Recognition Based on Expert Knowledge. Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA, USA, pp. 1429–1434. CrossrefGoogle Scholar
Sevdalis, V., & Keller, P. E.
(2009) Self-recognition in the perception of actions performed in synchrony with music. Annals of the New York Academy of Sciences, 1169, pp. 499–502. CrossrefGoogle Scholar
(2010) Cues for self-recognition in point-light displays of actions performed in synchrony with music. Consciousness and Cognition, 19, 2, pp. 617–626. CrossrefGoogle Scholar
Spanczer, I.
(2007) Observable Operator Models. Austrian Journal of Statistics, 36, 1, pp. 41–52. CrossrefGoogle Scholar
Tahboub, K. A.
(2005) Compliant Human-Robot Cooperation based on Intention Recognition. Proceedings of the 2005 IEEE International Symposium on Intelligent Control, Limassol, Cyprus, June 27–29, pp. 1417–1422.Google Scholar
(2006) Intelligent Human-Machine Interaction Based on Dynamic Bayesian Networks Probabilistic Intention Recognition. Journal of Intelligent Robotics Systems, 45, 1, pp. 31–52. CrossrefGoogle Scholar
Terada, K., Shamoto, T., Mei, H., & Ito, A.
(2007) Reactive Movements of Non-humanoid Robots Cause Intention Attribution in Humans. Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA, USA, pp. 3715–3720. CrossrefGoogle Scholar
Viterbi, A. J.
(1967) “Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm”, IEEE Transactions on Information Theory, 13, 2, pp. 260–269. CrossrefGoogle Scholar
[ p. 559 ]
Webb, T. L., & Sheeran, P.
(2006) Does changing behavioral intentions engender behavior change? A metaanalysis of the experimental evidence. Psychological Bulletin, 132, 2, pp. 249–268. CrossrefGoogle Scholar
Zhang, D., Gatica-Perez, D., Bengio, S., McCowan, I., & Lathoud, G.
(2004) Modeling Individual and Group Actions in Meetings: A Two-Layer HMM Framework. In Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshop, pp. 117–117.Google Scholar
[ p. 560 ]