Emergence of structured interactions: From a theoretical model to pragmatic robotics

In this article, we present two neural architectures for the control of socially interacting robots. Beginning with a theoretical model of interaction inspired by developmental psychology, biology and physics, we present two sub-cases of the model that can be interpreted as "turn-taking" and "synchrony" at the behavioral level. These neural architectures are both detailed and tested in simulation. A robotic experiment is even presented for the "turn-taking" case. We then discuss the interest of such behaviors for the development of further social abilities in robots.

[1]  William A. MacKay,et al.  Synchronized neuronal oscillations and their role in motor processes , 1997, Trends in Cognitive Sciences.

[2]  Philippe Gaussier,et al.  The Role of Internal Oscillators for the One-Shot Learning of Complex Temporal Sequences , 2007, ICANN.

[3]  Philippe Gaussier,et al.  Autonomous learning and reproduction of complexsequences: a multimodal architecture forbootstraping imitation games , 2005 .

[4]  Gordon Cheng,et al.  Complex continuous meaningful humanoid interaction: a multi sensory-cue based approach , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[5]  W. Singer,et al.  Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties , 1989, Nature.

[6]  Robert J. Wood,et al.  Towards a 3g crawling robot through the integration of microrobot technologies , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[7]  Instructions concerning the use of pendulum-watches, for finding the longitude at sea , 1669, Philosophical Transactions of the Royal Society of London.

[8]  Heloir,et al.  The Uncanny Valley , 2019, The Animation Studies Reader.

[9]  Hermann Haken,et al.  Information and Self-Organization: A Macroscopic Approach to Complex Systems , 2010 .

[10]  J. Issartel,et al.  Unintended interpersonal co-ordination: “can we march to the beat of our own drum?” , 2007, Neuroscience Letters.

[11]  Rolf Pfeifer,et al.  Understanding intelligence , 2020, Inequality by Design.

[12]  Jürgen Kurths,et al.  Complex Phase Synchronization in Neurophysiological Data , 1999 .

[13]  E. Niebur,et al.  Growth patterns in the developing brain detected by using continuum mechanical tensor maps , 2022 .

[14]  Philippe Gaussier,et al.  SPACE-TIME , ORDER , and HIERARCHYin FRONTO-HIPPOCAMPAL SYSTEM : A NEURAL BASIS OF PERSONALITY , 1997 .

[15]  T. Vicsek,et al.  Self-organizing processes: The sound of many hands clapping , 2000, Nature.

[16]  Jacqueline Nadel,et al.  New Perspectives in Early Communicative Development , 1993 .

[17]  Fernando Poyatos,et al.  New perspectives in nonverbal communication : studies in cultural anthropology, social psychology, linguistics, literature, and semiotics , 1983 .

[18]  Auke Jan Ijspeert,et al.  A neuromechanical investigation of salamander locomotion , 2000 .

[19]  R. Moore,et al.  A Clock for the Ages , 1999, Science.

[20]  J Kurths,et al.  Chaotic channel. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Arnold J. Mandell,et al.  Synergetics of the Brain , 1983 .

[22]  Jun Nakanishi,et al.  Movement imitation with nonlinear dynamical systems in humanoid robots , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[23]  J. Banquet,et al.  Chapter 4 Space-time, order, and hierarchy in fronto-hippocampal system: A neural basis of personality , 1997 .

[24]  Sorin Moga,et al.  From Perception-Action Loops to Imitation Processes: A Bottom-Up Approach of Learning by Imitation , 1998, Appl. Artif. Intell..

[25]  Vladimir I. Arnold,et al.  Remarks on the perturbation theory for problems of Mathieu type , 1983 .

[26]  Philippe Gaussier,et al.  Neurobiologically Inspired Mobile Robot Navigation and Planning , 2007, Frontiers in neurorobotics.

[27]  Philippe Gaussier,et al.  Human-Robot Interactions as a Cognitive Catalyst for the Learning of Behavioral Attractors , 2007, RO-MAN 2007 - The 16th IEEE International Symposium on Robot and Human Interactive Communication.

[28]  C. Uhl.  Analysis of neurophysiological brain functioning , 1999 .

[29]  Cynthia Breazeal,et al.  Regulation and Entrainment in Human—Robot Interaction , 2000, Int. J. Robotics Res..

[30]  Paolo Sassone-Corsi,et al.  Molecular clocks: mastering time by gene regulation , 1998, Nature.

[31]  L. Matthews.  Cognitive science perspectives on personality and emotion , 1997 .

[32]  G. Laurent,et al.  Impaired odour discrimination on desynchronization of odour-encoding neural assemblies , 1997, Nature.

[33]  Ken Prepin,et al.  Human–machine interaction as a model of machine–machine interaction: how to make machines interact as humans do , 2007, Adv. Robotics.

[34]  Peter H. Richter.  Information and Self-organization: A Macroscopic Approach to Complex Systems, Hermann Haken. Springer, New York (1988), $59.50 (cloth), 196 pp , 1991 .

[35]  A. Meltzoff,et al.  Imitation of Facial and Manual Gestures by Human Neonates , 1977, Science.

[36]  Sorin Moga,et al.  From Perception-Action loops to imitation processes , 1998 .

[37]  R. G. Medhurst,et al.  Topics in the Theory of Random Noise , 1969 .

[38]  Jürgen Kurths,et al.  Synchronization - A Universal Concept in Nonlinear Sciences , 2001, Cambridge Nonlinear Science Series.

[39]  M. Hallett.  Tremor , 1991, Neurology.

[40]  Luc Berthouze,et al.  Motor Skill Acquisition Under Environmental Perturbations: On the Necessity of Alternate Freezing and Freeing of Degrees of Freedom , 2004, Adapt. Behav..

[41]  Frank J. Bernieri,et al.  Synchrony, pseudosynchrony, and dissynchrony: Measuring the entrainment process in mother-infant interactions. , 1988 .

[42]  K Niizeki,et al.  Interaction among cardiac, respiratory, and locomotor rhythms during cardiolocomotor synchronization. , 1993, Journal of applied physiology.

[43]  杨晨航,et al.  分数阶Relaxation—Oscillation方程的一种分数阶预估-校正方法 , 2005 .

[44]  W Singer,et al.  Visual feature integration and the temporal correlation hypothesis. , 1995, Annual review of neuroscience.

[45]  Jacqueline Nadel,et al.  Imitation in infancy , 2011 .

[46]  J. Kelso,et al.  From the Cover : The phi complex as a neuromarker of human social coordination , 2007 .

[47]  Balth. van der Pol Jun..  LXXXVIII. On “relaxation-oscillations” , 1926 .

[49]  J. Kelso,et al.  Social coordination dynamics: Measuring human bonding , 2008, Social neuroscience.

[50]  G. Kugiumutzakis,et al.  Genesis and development of early infant mimesis to facial and vocal models. , 1999 .

[51]  J. Peery.  Neonate-adult head movement: No and yes revisited. , 1980 .

[52]  Albert-László Barabási,et al.  The sound of many hands clapping: Tumultuous applause can transform itself into waves of synchronized clapping. , 2000 .

[53]  Philippe Gaussier,et al.  Orientation system in Robots: Merging Allothetic and Idiothetic Estimations , 2007 .

[54]  J. Nadel,et al.  Imitation et communication entre jeunes enfants , 1986 .

[55]  J. Schnakenberg,et al.  G. Nicolis und I. Prigogine: Self‐Organization in Nonequilibrium Systems. From Dissipative Structures to Order through Fluctuations. J. Wiley & Sons, New York, London, Sydney, Toronto 1977. 491 Seiten, Preis: £ 20.–, $ 34.– , 1978 .

[56]  C. R. Taylor,et al.  A companion to Animal physiology , 1982 .

[57]  Wolf Singer,et al.  Striving for coherence , 1999 .

[58]  Hideki Kozima,et al.  Can a robot empathize with people? , 2004, Artificial Life and Robotics.

[59]  G. Butterworth.  Neonatal imitation: Existence, mechanisms and motives. , 1999 .