PLEASE NOTE: The dates for FTC 2019 have changed to 24-25 October, 2019. The change was necessitated due to venue availability.

Profile

Ignazio Infantino

Ignazio Infantino

National Research Council (CNR, Italy)

Ignazio Infantinno is Research Scientist at Institute of High Performance Computing and Networking (ICAR ), National Research Council (CNR), Italy. E-mail: [email protected] Page: http://www.pa.icar.cnr.it/infantino/ Fields of interest: cognitive robotics, affective robotics, computer vision, human-robot interaction Relevant publications: I. Infantino, G. Pilato, R. Rizzo, F. Vella, “Humanoid Introspection: a practical approach”, Intl. Journal of Advanced Robotic Systems: Human Machine Interaction, InTech, 2013 I. Infantino, “Affective human-humanoid interaction through cognitive architecture”, The Future of Humanoid Robots: Research and Applications, Riadh Zaier (Ed.), ISBN: 978-953-307-951-6, InTech, 2012. A. Chella, H.Dindo, I. Infantino, “Cognitive approach to goal-level imitation”, Interaction Studies: Social Behaviour and Communication in Biological and Artificial Systems, vol. 9, n. 2, pp. 301-318, 2008. I. Infantino, R. Rizzo, S. Gaglio, “A framework for sign language sentence recognition by common sense context”, IEEE Transactions on Systems, Man, and Cybernetics: Part C, vol. 37, issue 5, pp. 1034-1039, 2007. A. Chella, H. Dindo, I. Infantino, “Imitation Learning and Anchoring through Conceptual Spaces”, Applied Artificial Intelligence, vol. 21, issue 4 &5, pp. 343-359, 2007. A. Chella, H. Dindo, I. Infantino, “A Cognitive Framework for Imitation Learning”, Robotics and Autonomous Systems, special issue on “ Robot Programming by Demonstration”, vol. 54, n.5, pp. 403-408, 2006. I. Infantino, A. Chella, H. Dindo, “A Cognitive Architecture for Robotic Hand Posture Learning”, IEEE Transactions on Systems, Man, and Cybernetics: Part C, vol. 35, n.1, pp. 42-52, 2005. A. Chella, H. Dindo, I. Infantino, I. Macaluso, “A Posture Sequence Learning System for an Anthropomorphic Robotic Hand”, Robotics and Autonomous Systems, special issue on „Robot Learning by Demonstration”, vol. 47, n. 2-3 pp. 143-152, 2004.>