Symbol Emergence in Robotics: Probabilistic Generative Models for Real-world Multimodal Language Acquisition and Understanding
Par Tadahiro Taniguchi
Lien zoom (prière de bien identifier votre nom et prénom, une salle d’attente sera active): https://uqam.zoom.us/j/84473395235
Jeudi le 18 mars 2021 à 10h30
Symbol emergence in robotics aims to develop a robot that can adapt to the real-world environment, human linguistic communications, and acquire language from sensorimotor information alone, i.e., in an unsupervised manner. This line of studies is essential not only for creating a robot that can collaborate with people through human-robot interactions but also for understanding human cognitive development. This invited lecture introduces the recent development of integrative probabilistic generative models for language learning, e.g., spatial concept formation with simultaneous localization and mapping, and vision of symbol emergence in robotics. I will also introduce challenges related to the integration of probabilistic generative models and deep learning for language learning by robots.
Tadahiro Taniguchi received the ME and Ph.D. degrees from Kyoto University, in 2003 and 2006 respectively. From April 2008 to March 2010, he was an Assistant Professor at the Department of Human and Computer Intelligence, Ritsumeikan University. From April 2010 to March 2017, he was an Associate Professor at the same department. From September 2015 to September 2016, was a Visiting Associate Professor at the Department of Electrical and Electronic Engineering, Imperial College London. From April 2017, he has been a Professor at the Department of Information Science and Engineering, Ritsumeikan University. From April 2017, he has been a visiting general chief scientist, Technology Division, Panasonic, as well. He has been engaged in research on AI, symbol emergence in robotics, machine learning, and cognitive science.