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GRASP Special Seminar: Sergey Levine, UC Berkeley, “Robots that Learn by Doing”

February 13, 2018 @ 12:00 pm - 1:00 pm


Advances in machine learning have made it possible to build algorithms that can make complex and accurate inferences for open-world perception problems, such as recognizing objects in images or recognizing words in human speech. These advances have been enabled by improvements in models and algorithms, such as deep neural networks, advances in the amount of available computation and, crucially, the availability of large amounts of manually-labeled data. However, when we consider how we might build intelligent machines that can act, rather than just perceive, the requirement for massive human-labeled data becomes onerous and, in many cases, prohibitive. In this talk, I will discuss research in my group that aims to make learning fully autonomous, by enabling robots to improve continuously from experience that they collect on their own, either by attempting tasks in the real world, or simply by watching humans acting in their natural environment.


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Sergey Levine received a BS and MS in Computer Science from Stanford University in 2009, and a Ph.D. in Computer Science from Stanford University in 2014. He joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in fall 2016. His work focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms. Applications of his work include autonomous robots and vehicles, as well as computer vision and graphics. His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, deep reinforcement learning algorithms, and more.


February 13, 2018
12:00 pm - 1:00 pm
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