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Poster
Visual Memory for Robust Path Following
Ashish Kumar · Saurabh Gupta · David Fouhey · Sergey Levine · Jitendra Malik

Wed Dec 05 07:45 AM -- 09:45 AM (PST) @ Room 517 AB #122
in Wed Poster Session A »

Humans routinely retrace paths in a novel environment both forwards and backwards despite uncertainty in their motion. This paper presents an approach for doing so. Given a demonstration of a path, a first network generates a path abstraction. Equipped with this abstraction, a second network observes the world and decides how to act to retrace the path under noisy actuation and a changing environment. The two networks are optimized end-to-end at training time. We evaluate the method in two realistic simulators, performing path following and homing under actuation noise and environmental changes. Our experiments show that our approach outperforms classical approaches and other learning based baselines.

Keywords: Computer Vision
[ Paper] 

Author Information

Ashish Kumar (UC Berkeley)
Saurabh Gupta (UC Berkeley / FAIR / UIUC)
David Fouhey (UC Berkeley)
Sergey Levine (UC Berkeley)
Sergey Levine

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 applications in other decision-making domains. 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

Jitendra Malik (University of California at Berkley)

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