Scalable robust learning from demonstration with leveraged deep neural networks

Sungjoon Choi, Kyungjae Lee, Songhwai Oh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose a novel algorithm for learning from demonstration, which can learn a policy function robustly from a large number of demonstrations with mixed qualities. While most of the existing approaches assume that demonstrations are collected from skillful experts, the proposed method alleviates such restrictions by estimating the proficiency level of each demonstration using the proposed leverage optimization. Furthermore, a novel leveraged cost function is proposed to represent a policy function using deep neural networks by reformulating the objective function of leveraged Gaussian process regression using the representer theorem. The proposed method is successfully applied to autonomous track driving tasks, where a large number of demonstrations with mixed qualities are given as training data without labels.

Original languageEnglish
Title of host publicationIROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3926-3931
Number of pages6
ISBN (Electronic)9781538626825
DOIs
Publication statusPublished - 2017 Dec 13
Externally publishedYes
Event2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017 - Vancouver, Canada
Duration: 2017 Sep 242017 Sep 28

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
Volume2017-September
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Other

Other2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017
Country/TerritoryCanada
CityVancouver
Period17/9/2417/9/28

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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