Visual Memory For Robust Path Following

Authors:
Ashish Kumar UC Berkeley
Saurabh Gupta UC Berkeley / FAIR / UIUC
David Fouhey UC Berkeley
Sergey Levine UC Berkeley
Jitendra Malik University of California at Berkley

Introduction:

Humans routinely retrace a path in a novel environment both forwards and backwards despite uncertainty in their motion.In this paper, the authors present an approach for doing so.

Abstract:

Humans routinely retrace a path in a novel environment both forwards and backwards despite uncertainty in their motion. In this paper, we present an approach for doing so. Given a demonstration of a path, a first network generates an abstraction of the path. Equipped with this abstraction, a second network then observes the world and decides how to act in order 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 both forwards and backwards. Our experiments show that our approach outperforms both a classical approach to solving this task as well as a number of other baselines.

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