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Driven Learning for Driving: How Introspection Improves Semantic Mapping

Triebel, Rudolph and Grimmett, Hugo and Paul, Rohan and Posner, Ingmar (2016) Driven Learning for Driving: How Introspection Improves Semantic Mapping. In: Robotics Research, The 16th International Symposium ISRR Springer Tracts in Advanced Robotics, 114. Springer International Publishing Switzerland. pp. 449-465. doi: 10.1007/978-3-319-28872-7_26. ISBN 978-3-3 19-28870-3.

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Abstract

This paper explores the suitability of commonly employed classification methods to action-selection tasks in robotics, and argues that a classifier's introspective capacity is a vital but as yet largely under-appreciated attribute. As illustration we propose an active learning framework for semantic mapping in mobile robotics and demonstrate it in the context of autonomous driving. In this framework, data are selected for label disambiguation by a human supervisor using uncertainty sampling. Intuitively, an introspective classification framework - i.e. one which moderates its predictions by an estimate of how well it is placed to make a call in a particular situation-is particularly well suited to this task. To achieve an efficient implementation we extend the notion of introspection to a particular sparse Gaussian Process Classifier, the Informative Vector Machine (IVM). Furthermore, we leverage the information-theoretic nature of the IVM to formulate a principled mechanism for forgetting stale data, thereby bounding memory use and resulting in a truly lifelong learning system. Our evaluation on a publicly available dataset shows that an introspective active learner asks more informative questions compared to a more traditional non-introspective approach like a Support Vector Machine (SVM) and in so doing, outperforms the SVM in terms of learning rate while retaining efficiency for practical use.

Item URL in elib:https://elib.dlr.de/110074/
Document Type:Book Section
Title:Driven Learning for Driving: How Introspection Improves Semantic Mapping
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Triebel, RudolphRudolph.Triebel (at) dlr.deUNSPECIFIED
Grimmett, Hugohugo (at) robots.ox.ac.ukUNSPECIFIED
Paul, Rohanrohanp (at) robots.ox.ac.ukUNSPECIFIED
Posner, Ingmarhip (at) robots.ox.ac.ukUNSPECIFIED
Date:December 2016
Journal or Publication Title:Robotics Research, The 16th International Symposium ISRR
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Volume:114
DOI :10.1007/978-3-319-28872-7_26
Page Range:pp. 449-465
Publisher:Springer International Publishing Switzerland
Series Name:Springer Tracts in Advanced Robotics
ISBN:978-3-3 19-28870-3
Status:Published
Keywords:Semantic Mapping
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - On-Orbit Servicing [SY]
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013)
Deposited By: Beinhofer, Gabriele
Deposited On:02 Jan 2017 13:40
Last Modified:02 Jan 2017 13:40

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