Object-oriented Perception and Control
Why are infants better at sensory motor tasks than our current AI systems? We are born with learning mechanisms to map our sensory experience into objects and abstractions over them. My talk will present unsupervised approaches integrating deep/reinforcement learning and probabilistic programming, to learn about objects and goal-directed control grounded in them. I will give demonstrations in a few domains including video understanding, game playing and robotics.
I am a Research Scientist at Google DeepMind. Previously, I was a PhD student at MIT under the supervision of Joshua Tenenbaum. I am primarily interested in understanding how the mind works. My current research goal is to build learning algorithms that acquire grounded common-sense knowledge.