Continually Evolving Machines: Learning by Experimenting
An open question in artificial intelligence is how to endow agents with common sense knowledge that humans naturally seem to possess. A prominent theory in child development posits that human infants gradually acquire such knowledge through the process of experimentation. According to this theory, even the seemingly frivolous play of infants is a mechanism for them to conduct experiments to learn about their environment. Inspired by this view of biological sensorimotor learning, I will present my work on building artificial agents that use the paradigm of experimentation to explore and condense their experience into models that enable them to solve new problems. I will discuss the effectiveness of my approach and open issues using case studies of a robot learning to push objects, manipulate ropes, finding its way in office environments and an agent learning to play video games merely based on the incentive of conducting experiments.
Pulkit earned his Ph.D. in computer science from UC Berkeley and co-founded SafelyYou Inc. He will be starting as an Assistant Professor at MIT in the Fall of 2019. His research interests span robotics, deep learning, computer vision, and computational neuroscience. Pulkit completed his bachelors in Electrical Engineering from IIT Kanpur and was awarded the Director’s Gold Medal. His work has appeared multiple times in MIT Tech Review, Quanta, New Scientist, NYPost, etc. He is a recipient of Signatures Fellow Award, Fulbright Science and Technology Award, Goldman Sachs Global Leadership Award, OPJEMS and Sridhar Memorial Prize among others. Pulkit holds a “Sangeet Prabhakar” (equivalent to bachelors in Indian classical music) and occasionally performs in music concerts.