Robots That Learn by Doing
Sergey Levine, UC Berkeley
March 25, 2019 | EmTech Digital
Sergey Levine shares an update on his work to develop robots capable of navigating the real world, teaching themselves to perform more complex and challenging tasks.
Sergey Levine, Assistant Professor, UC Berkeley
Sergey Levine received a BS and MS in computer science from Stanford University in 2009 and a PhD in computer science from Stanford 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. 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 networks, scalable algorithms for inverse-reinforcement learning, deep reinforcement-learning algorithms, and more. His work has been featured in many popular press outlets, including the New York Times, the BBC, MIT Technology Review, and Bloomberg.
Artificial intelligence and robots are transforming how we work and live.
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