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Introducing CARESSER: A framework for in situ learning robot social assistance from expert knowledge and
Antonio Andriella1, Carme Torras1, Carla Abdelnour2
1CSIC-UPC, Institut de Robòtica i Informàtica Industrial, C/Llorens i Artigas 4-6, 08028 Barcelona, Spain.
User Modeling and User-Adapted Interaction
|March 21, 2022
Summary
Socially assistive robots, using the CARESSER framework, learn from therapists to provide personalized cognitive therapy support. This approach enhances patient performance and matches therapist preferences, improving care for those with cognitive impairments.
Area of Science:
- Robotics
- Artificial Intelligence
- Cognitive Science
Background:
- Socially assistive robots can aid therapists in repetitive cognitive tasks.
- Limited domain expert involvement and robot behavior automation hinder robot effectiveness.
Purpose of the Study:
- Introduce CARESSER, a novel framework for learning assistive robot behavior.
- Enable fast, autonomous, and personalized robot learning for cognitive therapies.
Main Methods:
- CARESSER uses a hybrid approach, combining therapist expertise (knowledge-driven) and demonstrations (data-driven).
- Two user studies were conducted with older adults experiencing mild dementia or cognitive impairment.
Main Results:
- Robots with CARESSER maintained patient performance, outperforming therapists.
- Robot-assisted sessions aligned with therapist preferences for assistance.
Conclusions:
- CARESSER's stakeholder-centric design enables AI to learn from human-human interactions and expertise.
- This approach accelerates learning, simplifies reward function design, and prevents undesirable states.
Keywords:
Human–robot interactionIn situ learningRobot adaptivityRobot personalisationRobot-assisted cognitive trainingSocially assistive roboticsMore Related Videos
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