Robust learning from demonstration using leveraged Gaussian processes and sparse-constrained optimization

Sungjoon Choi, Kyungjae Lee, Songhwai Oh

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

18 Citations (Scopus)

Abstract

In this paper, we propose a novel method for robust learning from demonstration using leveraged Gaussian process regression. While existing learning from demonstration (LfD) algorithms assume that demonstrations are given from skillful experts, the proposed method alleviates such assumption by allowing demonstrations from casual or novice users. To learn from demonstrations of mixed quality, we present a sparse-constrained leveraged optimization algorithm using proximal linearized minimization. The proposed sparse constrained leverage optimization algorithm is successfully applied to sensory field reconstruction and direct policy learning for planar navigation problems. In experiments, the proposed sparse-constrained method outperforms existing LfD methods.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Robotics and Automation, ICRA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages470-475
Number of pages6
ISBN (Electronic)9781467380263
DOIs
Publication statusPublished - 2016 Jun 8
Externally publishedYes
Event2016 IEEE International Conference on Robotics and Automation, ICRA 2016 - Stockholm, Sweden
Duration: 2016 May 162016 May 21

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2016-June
ISSN (Print)1050-4729

Other

Other2016 IEEE International Conference on Robotics and Automation, ICRA 2016
Country/TerritorySweden
CityStockholm
Period16/5/1616/5/21

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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