EUCognition Meeting 23-24.11.2017 in Zurich

On the left: «Alte Aula» / On the right: «Alte Aula, inside» On the left: «Alte Aula» / On the right: «Alte Aula, inside»

"Learning: Beyond Deep Neural Networks"


University of Zurich
Rämistrasse 59
8001 Zürich


Yulia Sandamirskaya, Raphaela Kreiser, Elisa Donatti (local chairs)
Vincent C. Müller (general chair)
Ron Chrisley


Registration via iniForum.

The computational framework of Deep Neural Networks (DNNs) has “taken off” in recent years because of the availability of immense computing resources and labeled data sets. The DNNs promise to solve many if not all problems in the field of artificial intelligence and lead to machines that can understand speech and images as well, or even better, than humans do. However, the current "hype" draws attention away from the limitations of the approach: despite of the impressive performance on selected datasets, there are fields of machine intelligence, where DNNs may not be a solution. One such field is cognitive robotics. The limitations in terms of time, energy, and computing power, characteristic of autonomous robotic systems that can support humans in their daily life and in hazardous environments, limit application of the computation-, data-, and energy-“hungry” DNNs in this field. Moreover, to be able to work flexibly and adaptively in real-world environments, shared and co-habited with humans, robotic systems require different types of learning, which work based on the sensory information acquired by the system in a closed behavioral loop, instead of the labeled data. This learning processes have to be real-time (fast) and tightly coupled to other sensorimotor and cognitive processes, such as perception, attention, memory formation, skill learning, or decision making.

The EUCognition 2017 conference will spawn a debate on the topic of machine learning technique in "embodied" (i.e. robotic) cognitive systems. We hope to highlight the challenges that the connectionist (neural network-based) controllers in general and learning architectures in particular face in robotics.

Funding by the UZH Graduate Campus via a GRC Grant is gratefully acknowledged