Reusing risk-aware stochastic abstract policies in robotic navigation learning

Valdinei Freire Da Silva, Marcelo Li Koga, Fábio Gagliardi Cozman, Anna Helena Reali Costa

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

1 Scopus citations

Abstract

In this paper we improve learning performance of a risk-aware robot facing navigation tasks by employing transfer learning; that is, we use information from a previously solved task to accelerate learning in a new task. To do so, we transfer risk-aware memoryless stochastic abstract policies into a new task. We show how to incorporate risk-awareness into robotic navigation tasks, in particular when tasks are modeled as stochastic shortest path problems. We then show how to use a modified policy iteration algorithm, called AbsProb-PI, to obtain risk-neutral and risk-prone memoryless stochastic abstract policies. Finally, we propose a method that combines abstract policies, and show how to use the combined policy in a new navigation task. Experiments validate our proposals and show that one can find effective abstract policies that can improve robot behavior in navigation problems. © 2014 Springer-Verlag Berlin Heidelberg.
Original languageAmerican English
Title of host publicationReusing risk-aware stochastic abstract policies in robotic navigation learning
Pages256-267
Number of pages12
ISBN (Electronic)9783662444672
DOIs
StatePublished - 1 Jan 2014
Externally publishedYes
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2018 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8371 LNAI
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/18 → …

Fingerprint Dive into the research topics of 'Reusing risk-aware stochastic abstract policies in robotic navigation learning'. Together they form a unique fingerprint.

Cite this