Probabilistic failure isolation for cognitive robots

Dogan Altan, Sanem Sariel-Talay

Research output: Contribution to conferencePaperpeer-review

8 Citations (Scopus)

Abstract

Robots may encounter undesirable outcomes due to failures during the execution of their plans in the physical world. Failures should be detected, and the underlying reasons should be found by the robot in order to handle these failure situations efficiently. Sometimes, there may be more than one cause of a failure, and they are not necessarily related to the action in execution. In this paper, we propose a temporal and Hierarchical Hidden Markov Model (HHMM) based failure isolation method. These HHMMs run in parallel to determine causes of unexpected deviations. Experiments on our Pioneer 3-AT robot show that our method successfully isolates failures suggesting possible causes.

Original languageEnglish
Pages370-375
Number of pages6
Publication statusPublished - 2014
Event27th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2014 - Pensacola, United States
Duration: 21 May 201423 May 2014

Conference

Conference27th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2014
Country/TerritoryUnited States
CityPensacola
Period21/05/1423/05/14

Bibliographical note

Publisher Copyright:
Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

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