Using probabilistic context awareness in a deliberative planner system
- Autores: Jonatan Ginés Clavero, Francisco José Rodríguez Campo, Martín Rico Ortega, Ángel Manuel Guerrero Higueras, Vicente Matellán Olivera
- Localización: From Bioinspired Systems and Biomedical Applications to Machine Learning: 8th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, Almería, Spain, June 3–7, 2019, Proceedings, Part II / coord. por Hojjat Adeli; José Manuel Ferrández Vicente (dir. congr.), José Ramón Álvarez Sánchez (dir. congr.), Félix de la Paz López (dir. congr.), Francisco Javier Toledo Moreo (dir. congr.), 2019, ISBN 978-3-030-19651-6, págs. 157-166
- Idioma: inglés
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Resumen
When a Social Robot is deployed in a service environment ithas to manage a highly dynamic scenarios that provide a set of unknown circumstances: objects in different places and humans walking around.These conditions are challenging for an autonomous robot that needs to accomplish assistive tasks. These partially known scenarios has negative effects on hybrid architectures with deliberative planning systems adding extra sub-tasks to main goal or continuous re-planing with deadlocks.This paper proposes the use of a probabilistic Context AwarenessSystem that provides a set of belief states of the environment to a symbolic planner enabling PDDL metrics.The Context Awareness System is composed by a Deep Learning classifier to process audio input from the environment, and an inference probabilistic module for generating symbolic knowledge. This approach delivers a method to generate correct plans efficiently. The solution presented in this paper is being successfully applied in a robot running Robot Operating System (ROS) on two experimental scenarios that illustrates the utility of the technique showing a reduction on execution time.
