Learning sensorimotor control from experience and demonstration
Alexey Dosovitskiy
(Intel Visual Computing Lab Munich, Germany)
Abstract:
An intelligent agent should be capable of performing useful actions based
on sensory observations, a feat known as sensorimotor control. I will
talk about two general approaches to learning sensorimotor control,
learning from experience and learning from demonstration and about
recent research projects in our lab in both of these directions. In one,
we train an agent to navigate in three-dimensional environments based
purely on its experience, without any human supervision. In another, we
use imitation learning to train an agent to drive in busy urban
environments and follow passenger's commands.
Bio:
Alexey Dosovitskiy received his MSc and PhD degrees in mathematics
(functional analysis) from Moscow State University in 2009 and 2012
respectively. He spent 2013-2015 as a postdoctoral researcher at the
Computer Vision Group of Prof. Thomas Brox at the University of Freiburg
in Germany, with research focus on deep learning, specifically
unsupervised learning, image generation with neural networks, motion and
3D structure estimation. Since May 2016 Alexey works on deep learning and
sensorimotor control at Intel Visual Computing Lab led by Dr. Vladlen
Koltun.