Visual memory for robust path following

Ashish Kumar, Saurabh Gupta, David Fouhey, Sergey Levine, Jitendra Malik

Research output: Contribution to journalConference article

Abstract

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.

Original languageEnglish (US)
Pages (from-to)765-774
Number of pages10
JournalAdvances in Neural Information Processing Systems
Volume2018-December
StatePublished - 2018
Event32nd Conference on Neural Information Processing Systems, NeurIPS 2018 - Montreal, Canada
Duration: Dec 2 2018Dec 8 2018

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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    Kumar, A., Gupta, S., Fouhey, D., Levine, S., & Malik, J. (2018). Visual memory for robust path following. Advances in Neural Information Processing Systems, 2018-December, 765-774.