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A Holistic Approach to Behavior Adaptation for Socially Assistive Robots

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Abstract

Socially assistive robotics aims at providing users with continuous support and personalized assistance, through appropriate social interactions. The design of robots capable of supporting people in heterogeneous tasks, raises several challenges among which the most relevant are the need to realise intelligent and continuous behaviours, robustness and flexibility of services and, furthermore, the ability to adapt to different contexts and needs. Artificial intelligence plays a key role in realizing cognitive capabilities like e.g., learning, context reasoning or planning that are highly needed in socially assistive robots. The integration of several of such capabilities is an open problem. This paper proposes a novel “cognitive approach” integrating ontology-based knowledge reasoning, automated planning and execution technologies. The core idea is to endow assistive robots with intelligent features in order to reason at different levels of abstraction, understand specific health-related needs and decide how to act in order to perform personalized assistive tasks. The paper presents such a cognitive approach pointing out the contribution of different knowledge contexts and perspectives, presents detailed functioning traces to show adaptation and personalization features, and finally discusses an experimental assessment proving the feasibility of the approach.

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Notes

  1. http://www.loa-cnr.it/ontologies/DUL.owl.

  2. https://protege.stanford.edu.

  3. The reasoning processes have been developed using the open-source Apache Jena library—https://jena.apache.org.

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Acknowledgements

The authors would like to express their gratitude to Stefano Borgo whose collaboration in a different project [10] turned out being influential for their approach in the research direction described in this paper.

Funding

At present, authors are partially supported by the SI-Robotics project - “SocIal ROBOTICS for active and healthy ageing”. A project funded by the Italian Ministry for Education and Research (PON MIUR - PNR 2015-2020): Area “Technology enhanced living environments”.

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Authors and Affiliations

  1. CNR – Italian National Research Council, ISTC – Institute of Cognitive Sciences and Technologies, Via S. Martino della Battaglia 44, 00185, Rome, Italy

    Alessandro Umbrico, Amedeo Cesta, Gabriella Cortellessa & Andrea Orlandini

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  1. Alessandro Umbrico
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  2. Amedeo Cesta
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  3. Gabriella Cortellessa
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Correspondence to Gabriella Cortellessa.

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Umbrico, A., Cesta, A., Cortellessa, G. et al. A Holistic Approach to Behavior Adaptation for Socially Assistive Robots. Int J of Soc Robotics 12, 617–637 (2020). https://doi.org/10.1007/s12369-019-00617-9

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