ArticleUser modeling and user-adapted interaction2023
Introducing CARESSER: A framework for in situ learning robot social assistance from expert knowledge and demonstrations.
Article in User modeling and user-adapted interaction, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 28 citations in OpenAlex.
- A Multimodal Adaptive Framework for Social Interaction with the MiRo-E Robot.Sensors (Basel, Switzerland) · 2026Article
- Personalized Cognitive Support via Social Robots.Sensors (Basel, Switzerland) · 2025Review
- Personalizing Care Through Robotic Assistance and Clinical Supervision.Frontiers in robotics and AI · 2022Article
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4 authors at 2 institutions in 1 country.
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Abstract
Socially assistive robots have the potential to augment and enhance therapist's effectiveness in repetitive tasks such as cognitive therapies. However, their contribution has generally been limited as domain experts have not been fully involved in the entire pipeline of the design process as well as in the automatisation of the robots' behaviour. In this article, we present aCtive leARning agEnt aSsiStive bEhaviouR (CARESSER), a novel framework that actively learns robotic assistive behaviour by leveraging the therapist's expertise (knowledge-driven approach) and their demonstrations (data-driven approach). By exploiting that hybrid approach, the presented method enables in situ fast learning, in a fully autonomous fashion, of personalised patient-specific policies. With the purpose of evaluating our framework, we conducted two user studies in a daily care centre in which older adults affected by mild dementia and mild cognitive impairment (
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