Abstract
In this paper we present a biologically-inspired model for social behavior recognition and generation. Based on an unified sensorimotor representation, it integrates hierarchical motor knowledge structures, probabilistic forward models for predicting observations, and inverse models for motor learning. With a focus on hand gestures, results of initial evaluations against real-world data are presented.
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References
Amit, R., Mataric, M.: Learning movement sequences from demonstration. In: ICDL 2002: Proceedings of the 2nd International Conference on Development and Learning, pp. 203–208 (2002)
Botvinick, M.M.: Hierarchical models of behavior and prefrontal function. Trends in Cognitive Sciences 12(5), 201–208 (2008), http://www.sciencedirect.com/science/article/B6VH9-4S95WHD-1/2/f02cfdf1fde7f8df4f4d2d52da7acd7b
Breazeal, C., Buchsbaum, D., Gray, J., Gatenby, D., Blumberg, B.: Learning from and about others: Towards using imitation to bootstrap the social understanding of others by robots. Artificial Life 11(1-2), 31–62 (2005), http://dx.doi.org/10.1162/1064546053278955
Calinon, S., Billard, A.: Recognition and Reproduction of Gestures using a Probabilistic Framework Combining PCA, ICA and HMM. In: 22nd International Conference on Machine Learning, pp. 105–112 (2005)
Dautenhahn, K.: Socially intelligent robots: Dimensions of human - robot interaction. Philosophical Transactions of the Royal Society B: Biological Sciences 362(1480), 679–704 (2007)
Dijksterhuis, A., Bargh, J.: The perception-behavior expressway: Automatic effects of social perception on social behavior. Advances in Experimental Social Psychology 33, 1–40 (2001)
Chen, F.-S., Fu, C.-M., Huang, C.L.: Hand gesture recognition using a real-time tracking method and hidden markov models. Image and Vision Computing 21, 745–758 (2003)
Hamilton, A., Grafton, S.: The motor hierarchy: From kinematics to goals and intentions. In: Attention and Performance, vol. 22. Oxford University Press, Oxford (2007)
Haruno, M., Wolpert, D.M., Kawato, M.: Mosaic model for sensorimotor learning and control. Neural Computation 13(10), 2201–2220 (2001), http://www.mitpressjournals.org/doi/abs/10.1162/089976601750541778
Haruno, M., Wolpert, D.M., Kawato, M.: Hierarchical mosaic for movement generation. International Congress Series 1250, 575–590 (2003), http://www.sciencedirect.com/science/article/B7581-49N7DHR-1J/2/83e9a135a8a183a9f18da5a66dcd3bbf ; Cognition and emotion in the brain. Selected topics of the International Symposium on Limbic and Association Cortical Systems
Johnson, M., Demiris, Y.: Hierarchies of coupled inverse and forward models for abstraction in robot action planning, recognition and imitation. In: Proceedings of the AISB 2005 Symposium on Imitation in Animals and Artifacts (2005)
Kopp, S., Graeser, O.: Imitation learning and response facilitation in embodied agents. In: Gratch, J., Young, M., Aylett, R.S., Ballin, D., Olivier, P. (eds.) IVA 2006. LNCS (LNAI), vol. 4133, pp. 28–41. Springer, Heidelberg (2006)
Kopp, S., Wachsmuth, I.: Synthesizing multimodal utterances for conversational agents. Journal of Computer Animation and Virtual Worlds 15(1), 39–52 (2004)
Kopp, S., Wachsmuth, I., Bonaiuto, J., Arbib, M.: Imitation in embodied communication – from monkey mirror neurons to artificial humans. In: Wachsmuth, I., Lenzen, M., Knoblich, G. (eds.) Embodied Communication in Humans and Machines, pp. 357–390. Oxford University Press, Oxford (2008)
Natalie Sebanz, G.K.: The role of the mirror system in embodied communication. In: Wachsmuth, I., Lenzen, M., Knoblich, G. (eds.) Embodied Communication in Humans and Machines, ch. 7, pp. 129–149. Oxford University Press, Oxford (2008)
Schutz-Bosbach, S., Prinz, W.: Perceptual resonance: action-induced modulation of perception. Journal of Trends in Cognitive Sciences 11(8), 349–355 (2007)
Wolpert, D.M., Doya, K., Kawato, M.: A unifying computational framework for motor control and social interaction. Philos Trans. R. Soc. Lond. B. Biol. Sci. 358(1431), 593–602 (2003), http://dx.doi.org/10.1098/rstb.2002.1238
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Sadeghipour, A., Yaghoubzadeh, R., Rüter, A., Kopp, S. (2009). Social Motorics – Towards an Embodied Basis of Social Human-Robot Interaction. In: Ritter, H., Sagerer, G., Dillmann, R., Buss, M. (eds) Human Centered Robot Systems. Cognitive Systems Monographs, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10403-9_20
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DOI: https://doi.org/10.1007/978-3-642-10403-9_20
Publisher Name: Springer, Berlin, Heidelberg
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