Hierarchical HMM-based Failure Isolation for Cognitive Robots

Dogan Altan, Sanem Sariel-Talay


Robots execute their planned actions in the physical world to accomplish their goals. However, since the real world is partially observable and dynamic, failures may occur during the execution of their actions. These failures should be detected immediately, and the underlying reasons of these failures should be isolated to ensure robustness. In this paper, we propose a probabilistic and temporal model-based failure isolation method that maintains Hierarchical Hidden Markov Models (HHMMs) in order to represent and reason about different failure types. The underlying reason of a failure can be isolated efficiently by multi-hypothesis tracking.


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Paper Citation

in Harvard Style

Altan D. and Sariel-Talay S. (2014). Hierarchical HMM-based Failure Isolation for Cognitive Robots . In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-016-1, pages 299-304. DOI: 10.5220/0004921502990304

in Bibtex Style

author={Dogan Altan and Sanem Sariel-Talay},
title={Hierarchical HMM-based Failure Isolation for Cognitive Robots},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},

in EndNote Style

JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Hierarchical HMM-based Failure Isolation for Cognitive Robots
SN - 978-989-758-016-1
AU - Altan D.
AU - Sariel-Talay S.
PY - 2014
SP - 299
EP - 304
DO - 10.5220/0004921502990304