Making robots learn to perceive and act with understanding

Abstract

The title of the talk is the motto of the Intelligent and Interactive Systems group at the University of Innsbruck. The long-term objective of the group is to improve abstraction, generalization, robustness, and ultimately explainability of robot perception and action. Motivated by shortcomings of current machine-learning methods, Prof. Piater will discuss examples of the group's recent work on learning visual relational concepts, extrapolation of learned movements beyond the training distribution, and learning of symbolic concepts and rules from sensorimotor experience. As an added bonus, Prof. Piater will show how to train neural networks of many hundreds of layers without skip connections, batch normalization, dropout, or any other architectural tweaks, using the so-called batch-entropy regularization.

Biosketch

Justus Piater is a professor of computer science at the University of Innsbruck (Austria) where he leads the Intelligent and Interactive Systems group.
He holds a M.Sc. degree from the University of Magdeburg (Germany) and M.Sc. and Ph.D. degrees from the University of Massachusetts Amherst (USA) all in computer science. Before joining the University of Innsbruck in 2010, he was a visiting researcher at the Max Planck Institute for Biological Cybernetics in Tübingen (Germany) a professor of computer science at the University of Liège (Belgium) and a Marie-Curie research fellow at GRAVIR-IMAG, INRIA Rhône-Alpes (France).
His research interests focus on learning and inference in sensorimotor systems. He has published more than 200 papers in international journals and conferences, several of which have received best-paper awards. Currently he serves as the founding director of the interdisciplinary Digital Science Center of the University of Innsbruck.

Streaming

The seminar will also be streamed live on the Zoom platform. Please contact Dr. Matteo Saveriano to have the link.