Abstract
Using service robots at home is becoming more and more popular in order to help people in their life routine. Such robots are required to do various tasks, from user notification to devices manipulation. However, in such complex environments, robots sometimes fail to achieve one task. Failing is problematic as it is unpleasant for the user and may cause critical situations. Therefore, understanding and preventing failures is a challenging need. In this paper, we propose LEAF, an experience based approach to prevent task failure. LEAF relies on both semantic context knowledge through ontology and user validation, allowing LEAF to have an accurate understanding of failures. It then uses this new knowledge to adapt a Hierarchical Task Network (HTN) in order to prevent selecting tasks that have a high risk of failure in the plan. LEAF was tested in the Hadaptic platform and evaluated using a randomly generated dataset.
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References
Al-Moadhen, A., Qiu, R., Packianather, M., Ji, Z., Setchi, R.: Integrating robot task planner with common-sense knowledge base to improve the efficiency of planning. Procedia Comput. Sci. 22, 211–220 (2013)
Bouneffouf, D.: Drars, a dynamic risk-aware recommender system. Ph.D. thesis, Institut National des Télécommunications (2013)
Fikes, R.E., Nilsson, N.J.: Strips: a new approach to the application of theorem proving to problem solving. Artif. Intell. 2(3), 189–208 (1972)
Garivier, A., Moulines, E.: On upper-confidence bound policies for switching bandit problems. In: International Conference on Algorithmic Learning Theory, pp. 174–188. Springer (2011)
Georgievski, I., Aiello, M.: An overview of hierarchical task network planning (2014). arXiv:1403.7426
Ghezala, M.W.B.: Compréhension dynamique du contexte pour l’aide à l’opérateur en robotique. Ph.D. thesis, Institut National des Télécommunications (2015)
Gouaillier, D., Hugel, V., Blazevic, P., Kilner, C., Monceaux, J., Lafourcade, P., Marnier, B., Serre, J., Maisonnier, B.: Mechatronic design of nao humanoid. In: IEEE International Conference on Robotics and Automation, 2009 (ICRA’09), pp. 769–774. IEEE (2009)
Hanheide, M., Göbelbecker, M., Horn, G.S., Pronobis, A., Sjöö, K., Aydemir, A., Jensfelt, P., Gretton, C., Dearden, R., Janicek, M., et al.: Robot task planning and explanation in open and uncertain worlds. Artif. Intell. (2015)
Kapotoglu, M., Koc, C., Sariel, S.: Robots avoid potential failures through experience-based probabilistic planning. In: 12th International Conference on Informatics in Control, Automation and Robotics (ICINCO), 2015, vol. 2, pp. 111–120. IEEE (2015)
Lallement, R., De Silva, L., Alami, R.: Hatp: An htn planner for robotics (2014). arXiv:1405.5345
Lassila, O., Swick, R.R., et al.: Resource description framework (rdf) model and syntax specification (1998)
Mahajan, A., Teneketzis, D.: Multi-armed bandit problems. Foundations and Applications of Sensor Management pp. 121–151 (2008)
Milliez, G., Lallement, R., Fiore, M., Alami, R.: Using human knowledge awareness to adapt collaborative plan generation, explanation and monitoring. In: The Eleventh ACM/IEEE International Conference on Human Robot Interaction, pp. 43–50. IEEE Press (2016)
Nau, D.S., Au, T.C., Ilghami, O., Kuter, U., Murdock, J.W., Wu, D., Yaman, F.: Shop2: An htn planning system. J. Artif. Intell. Res. (JAIR) 20, 379–404 (2003)
Ramoly, N., Bouzeghoub, A., Finance, B.: Context-aware planning by refinement for personal robots in smart homes. In: Proceedings of ISR 2016: 47st International Symposium on Robotics, pp. 1–8. VDE (2016)
Sariel, S., Yildiz, P., Karapinar, S., Altan, D., Kapotoglu, M.: Robust task execution through experience-based guidance for cognitive robots. In: International Conference on Advanced Robotics (ICAR), 2015, pp. 663–668. IEEE (2015)
Weser, M., Off, D., Zhang, J.: Htn robot planning in partially observable dynamic environments. In: IEEE International Conference on Robotics and Automation (ICRA), 2010, pp. 1505–1510. IEEE (2010)
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Ramoly, N., Sfar, H., Bouzeghoub, A., Finance, B. (2018). LEAF: Using Semantic Based Experience to Prevent Task Failures. In: Hutter, M., Siegwart, R. (eds) Field and Service Robotics. Springer Proceedings in Advanced Robotics, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-67361-5_44
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DOI: https://doi.org/10.1007/978-3-319-67361-5_44
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