publication . Bachelor thesis . 2016

Sociala sensorimotoriska funktioner

Bütepage, Judith;
Open Access English
  • Published: 01 Jan 2016
  • Publisher: KTH, Datorseende och robotik, CVAP
  • Country: Sweden
Abstract
As the field of robotics advances, more robots are employed in our everyday environment. Thus, the implementation of robots that can actively engage in physical collaboration and naturally interact with humans is of high importance. In order to achieve this goal, it is necessary to study human interaction and social cognition and how these aspects can be implemented in robotic agents. The theory of social sensorimotor contingencies hypothesises that many aspects of human-human interaction depend on low-level signalling and mutual prediction. In this thesis, I give an extensive account of these underlying mechanisms and how research in human-robot interaction has...
Subjects
free text keywords: Human-Robot interaction, social, intelligence, machine learning, robotics, Computer Sciences, Datavetenskap (datalogi)
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55 references, page 1 of 4

2 Interaction and Collaboration in Humans 17 2.1 Phenomena in Joint Action . . . . . . . . . . . . . . . . . . . . . 18 2.1.1 Emergent Coordination . . . . . . . . . . . . . . . . . . . 19 2.1.2 Planned Coordination . . . . . . . . . . . . . . . . . . . . 21 2.2 Theories of Embodied Interaction . . . . . . . . . . . . . . . . . . 24 2.2.1 Dynamical System Theory . . . . . . . . . . . . . . . . . . 24 2.2.2 Action - Perception Mapping and Action Simulation . . . 26 2.2.3 Bayesian methods - A computational framework . . . . . 29

3 Human-Robot Interaction 33 3.1 Embodied social intelligence - Learning of socSMCs . . . . . . . 33 3.1.1 Unsupervised learning of sensorimotor contingencies . . . 34 3.1.2 Supervised and inverse reinforcement learning . . . . . . . 36 3.1.3 Map Learning - The correspondence problem . . . . . . . 39 3.1.4 Emergent Social Learning . . . . . . . . . . . . . . . . . . 40 3.2 Interaction and Collaboration of Humans and Robots . . . . . . 43 3.2.1 Active collaboration . . . . . . . . . . . . . . . . . . . . . 43 3.3 Evaluating of interaction experiments . . . . . . . . . . . . . . . 47 3.3.1 Human-centred Human-Robot Interaction . . . . . . . . . 49 4.2.1 Learning how to be human - An example . . . . . . . . . 67 4.2.2 Imitating and signalling . . . . . . . . . . . . . . . . . . . 69 4.2.3 Interacting on an eye-to-eye level . . . . . . . . . . . . . . 70 4.2.4 Fooling a human into believing . . . . . . . . . . . . . . . 72

5 Experiments 75 5.1 False beliefs and the Theory of mind . . . . . . . . . . . . . . . . 76 5.2 Active inference - A modelling approach . . . . . . . . . . . . . . 79 5.3 Past affordances - a simulation experiment . . . . . . . . . . . . . 82 5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 5.5 Discussion of experiments . . . . . . . . . . . . . . . . . . . . . . 86 5.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

6 Final thoughts 89 6.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 6.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 [15] Simon Baron-Cohen, A. M. Leslie, and U. Frith. Does the autistic child have a “theory of mind?”. Cognition, 21(1):37-46, 1985.

[32] Ergun Calisgan, A. Haddadi, H. F. M. Van der Loos, J. A. Alcazar, and E. A. Croft. Identifying nonverbal cues for automated human-robot turn-taking. In RO-MAN, 2012 IEEE, pages 418-423. IEEE, 2012.

[33] Tanya L. Chartrand and J. A. Bargh. The chameleon effect: the perceptionbehavior link and social interaction. Journal of personality and social psychology, 76(6):893-910, 1999. [OpenAIRE]

[34] Mark M. Churchland, J. P. Cunningham, M. T. Kaufman, J. D. Foster, P. Nuyujukian, S. I. Ryu, and K. V. Shenoy. Neural population dynamics during reaching. Nature, 487(7405):51-56, 2012. [OpenAIRE]

[35] Marcello Costantini, G. Committeri, and C. Sinigaglia. Ready both to your and to my hands: mapping the action space of others. PLoS One, 6(4):e17923, 2011.

[36] Sarah H. Creem-Regehr, K. T. Gagnon, M. N. Geuss, and J. K. Stefanucci. Relating spatial perspective taking to the perception of other's affordances: providing a foundation for predicting the future behavior of others. Frontiers in human neuroscience, 7, 2013.

[37] Christopher Crick, M. Munz, and B. Scassellati. Synchronization in social tasks: Robotic drumming. In Robot and Human Interactive Communication, 2006. ROMAN 2006. The 15th IEEE International Symposium on, pages 97-102. IEEE, 2006.

[38] Andreas C. Damianou and N. D. Lawrence. Deep gaussian processes. arXiv preprint arXiv:1211.0358, 2012. [OpenAIRE]

[39] Marie Devaine, G. Hollard, and J. Daunizeau. The social bayesian brain: does mentalizing make a difference when we learn? PLoS computational biology, 10(12):e1003992, 2014.

[40] Giuseppe Di Pellegrino, L. Fadiga, L. Fogassi, V. Gallese, and G. Rizzolatti. Understanding motor events: a neurophysiological study. Experimental brain research, 91(1):176-180, 1992. [OpenAIRE]

[41] Andreea O. Diaconescu, C. Mathys, L. A. Weber, J. Daunizeau, L. Kasper, E. I. Lomakina, E. Fehr, and K. E. Stephan. Inferring on the intentions of others by hierarchical bayesian learning. PLoS Comput Biol, 10(9):e1003810, 2014.

[42] H. Dindo, F. Donnarumma, F. Chersi, and G. Pezzulo. The intentional stance as structure learning: a computational perspective on mindreading. Biological cybernetics, 2015. [OpenAIRE]

55 references, page 1 of 4
Related research
Abstract
As the field of robotics advances, more robots are employed in our everyday environment. Thus, the implementation of robots that can actively engage in physical collaboration and naturally interact with humans is of high importance. In order to achieve this goal, it is necessary to study human interaction and social cognition and how these aspects can be implemented in robotic agents. The theory of social sensorimotor contingencies hypothesises that many aspects of human-human interaction depend on low-level signalling and mutual prediction. In this thesis, I give an extensive account of these underlying mechanisms and how research in human-robot interaction has...
Subjects
free text keywords: Human-Robot interaction, social, intelligence, machine learning, robotics, Computer Sciences, Datavetenskap (datalogi)
Related Organizations
Download from
55 references, page 1 of 4

2 Interaction and Collaboration in Humans 17 2.1 Phenomena in Joint Action . . . . . . . . . . . . . . . . . . . . . 18 2.1.1 Emergent Coordination . . . . . . . . . . . . . . . . . . . 19 2.1.2 Planned Coordination . . . . . . . . . . . . . . . . . . . . 21 2.2 Theories of Embodied Interaction . . . . . . . . . . . . . . . . . . 24 2.2.1 Dynamical System Theory . . . . . . . . . . . . . . . . . . 24 2.2.2 Action - Perception Mapping and Action Simulation . . . 26 2.2.3 Bayesian methods - A computational framework . . . . . 29

3 Human-Robot Interaction 33 3.1 Embodied social intelligence - Learning of socSMCs . . . . . . . 33 3.1.1 Unsupervised learning of sensorimotor contingencies . . . 34 3.1.2 Supervised and inverse reinforcement learning . . . . . . . 36 3.1.3 Map Learning - The correspondence problem . . . . . . . 39 3.1.4 Emergent Social Learning . . . . . . . . . . . . . . . . . . 40 3.2 Interaction and Collaboration of Humans and Robots . . . . . . 43 3.2.1 Active collaboration . . . . . . . . . . . . . . . . . . . . . 43 3.3 Evaluating of interaction experiments . . . . . . . . . . . . . . . 47 3.3.1 Human-centred Human-Robot Interaction . . . . . . . . . 49 4.2.1 Learning how to be human - An example . . . . . . . . . 67 4.2.2 Imitating and signalling . . . . . . . . . . . . . . . . . . . 69 4.2.3 Interacting on an eye-to-eye level . . . . . . . . . . . . . . 70 4.2.4 Fooling a human into believing . . . . . . . . . . . . . . . 72

5 Experiments 75 5.1 False beliefs and the Theory of mind . . . . . . . . . . . . . . . . 76 5.2 Active inference - A modelling approach . . . . . . . . . . . . . . 79 5.3 Past affordances - a simulation experiment . . . . . . . . . . . . . 82 5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 5.5 Discussion of experiments . . . . . . . . . . . . . . . . . . . . . . 86 5.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

6 Final thoughts 89 6.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 6.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 [15] Simon Baron-Cohen, A. M. Leslie, and U. Frith. Does the autistic child have a “theory of mind?”. Cognition, 21(1):37-46, 1985.

[32] Ergun Calisgan, A. Haddadi, H. F. M. Van der Loos, J. A. Alcazar, and E. A. Croft. Identifying nonverbal cues for automated human-robot turn-taking. In RO-MAN, 2012 IEEE, pages 418-423. IEEE, 2012.

[33] Tanya L. Chartrand and J. A. Bargh. The chameleon effect: the perceptionbehavior link and social interaction. Journal of personality and social psychology, 76(6):893-910, 1999. [OpenAIRE]

[34] Mark M. Churchland, J. P. Cunningham, M. T. Kaufman, J. D. Foster, P. Nuyujukian, S. I. Ryu, and K. V. Shenoy. Neural population dynamics during reaching. Nature, 487(7405):51-56, 2012. [OpenAIRE]

[35] Marcello Costantini, G. Committeri, and C. Sinigaglia. Ready both to your and to my hands: mapping the action space of others. PLoS One, 6(4):e17923, 2011.

[36] Sarah H. Creem-Regehr, K. T. Gagnon, M. N. Geuss, and J. K. Stefanucci. Relating spatial perspective taking to the perception of other's affordances: providing a foundation for predicting the future behavior of others. Frontiers in human neuroscience, 7, 2013.

[37] Christopher Crick, M. Munz, and B. Scassellati. Synchronization in social tasks: Robotic drumming. In Robot and Human Interactive Communication, 2006. ROMAN 2006. The 15th IEEE International Symposium on, pages 97-102. IEEE, 2006.

[38] Andreas C. Damianou and N. D. Lawrence. Deep gaussian processes. arXiv preprint arXiv:1211.0358, 2012. [OpenAIRE]

[39] Marie Devaine, G. Hollard, and J. Daunizeau. The social bayesian brain: does mentalizing make a difference when we learn? PLoS computational biology, 10(12):e1003992, 2014.

[40] Giuseppe Di Pellegrino, L. Fadiga, L. Fogassi, V. Gallese, and G. Rizzolatti. Understanding motor events: a neurophysiological study. Experimental brain research, 91(1):176-180, 1992. [OpenAIRE]

[41] Andreea O. Diaconescu, C. Mathys, L. A. Weber, J. Daunizeau, L. Kasper, E. I. Lomakina, E. Fehr, and K. E. Stephan. Inferring on the intentions of others by hierarchical bayesian learning. PLoS Comput Biol, 10(9):e1003810, 2014.

[42] H. Dindo, F. Donnarumma, F. Chersi, and G. Pezzulo. The intentional stance as structure learning: a computational perspective on mindreading. Biological cybernetics, 2015. [OpenAIRE]

55 references, page 1 of 4
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