Imitation and Social Learning in Robots, Humans and Animals

Chrystopher L. Nehaniv is Research Professor of Mathematical and Evolutionary Computer Sciences in the School of Computer Science at the University of Hertfordshire, where he works with the Adaptive Systems, Algorithms and BioComputation Research Groups. He is the Director of the UK EPSRC Network on Evolvability in Biological and Software Systems and an Associate Editor of BioSystems: Journal of Biological and Information Processing Sciences and Interaction Studies: Social Behaviour and Communication in Biological and Artificial Systems. Kerstin Dautenhahn is Research Professor of Artificial Intelligence in the School of Computer Science at the University of Hertfordshire, where she is a coordinator of the Adaptive Systems Research Group. Her research interests include social learning, human-robot interaction, social robotics, narrative and robotic assisted therapy for children with autism. She is the Editor-in-Chief of Interaction Studies: Social Behaviour and Communication in Biological and Artificial Systems and the general chair of the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2006).

• Introduction: The constructive interdisciplinary viewpoint for understanding mechanisms and models of imitation and social learning Kerstin Dautenhahn and Chrystopher L. Nehaniv; Part I. Correspondence Problems and Mechanisms: 1. Imitation: thoughts about theories Geoffrey Bird and Cecilia Heyes; 2. Nine billion correspondence problems Chrystopher L. Nehaniv; 3. Challenges and issues faced in building a framework for conducting research in learning from observation Darrin Bentivegna, Christopher Atkeson and Gordon Cheng; Part II. Mirroring and 'Mind-Reading': 4. A neural architecture for imitation and intentional relations Marco Iacoboni, Jonas Kaplan and Stephen Wilson; 5. Simulation theory of understanding others: a robotics perspective Yiannis Demiris and Matthew Johnson; 6. Mirrors and matchings: imitation from the perspective of mirror-self-recognition and the parietal region's involvement in both Robert W. Mitchell; Part III. What to Imitate: 7. The question of 'what to imitate': inferring goals and intentions from demonstrations Malinda Carpenter and Josep Call; 8. Learning of gestures by imitation in a humanoid robot Sylvain Calinon and Aude Billard; 9. The dynamic emergence of categories through imitation Tony Belpaeme, Bart de Boer and Bart Jansen; Part IV. Development and Embodiment: 10. Copying strategies by people with autistic spectrum disorder: why only imitation leads to social cognitive development Justin H. G. Williams; 11. A bayesian model of imitation in infants and robots Rajesh P. N. Rao, Aaron P. Shon and Andrew N. Meltzoff; 12. Solving the correspondence problem in robotic imitation across embodiments: synchrony, perception and culture in artefacts Aris Alissandrakis, Chrystopher L. Nehaniv and Kerstin Dautenhahn; Part V. Synchrony and Turn-Taking as Communicative Mechanisms: 13. How to build an imitator? Arnaud Revel and Jacqueline Nadel; 14. Simulated turn-taking and development of styles of motion Takashi Ikegami and Hiroki Iizuka; 15. Bullying behaviour, empathy and imitation: an attempted synthesis Kerstin Dautenhahn, Sarah N. Woods and Christina Kaouri; Part VI. Why Imitate? Motivations: 16. Multiple motivations for imitation in infancy Mark Nielsen and Virginia Slaughter; 17. The progress drive hypothesis: an interpretation of early imitation Frédéric Kaplan and Pierre-Yves Oudeyer; Part VII. Social Feedback: 18. Training behaviour by imitation: from parrots to people … to robots? Irene M. Pepperberg and Diane V. Sherman; 19. Task learning through imitation and human-robot interaction Monica N. Nicolescu and Maja J. Mataric; Part VIII. The Ecological Context: 20. Emulation learning: the integration of technical and social cognition Ludwig Huber; 21. Mimicry as deceptive resemblance: beyond the one-trick ponies Mark D. Norman and Tom Tregenza.

'Imitation and Social Learning in Robots, Humans, and Animals advances our understanding of the diversity of “imitations” and how much is to be learned from comparing them across species as diverse as parrots, butterflies, and even a male cuttlefish impersonating a female in a breeding pair – and thence to humans and their primate cousins and the brain mechanisms which support imitation and social learning. This book offers a rich set of processing strategies of importance to key areas of computer science, like robotics and embodied communication - and this new understanding factors back into novel theories of human social interaction and its disorders.' Michael Arbib, University Professor, Fletcher Jones Chair in Computer Science and Professor of Biological Sciences and Biomedical Engineering, University of Southern California