Affecta-Context: The Context-Guided Behavior Adaptation Framework
Morten Roed Frederiksen, Kasper St{\o}y

TL;DR
Affecta-Context is a framework that enables social robots to adapt their behaviors based on physical context and user preferences, learned through interaction, to improve human-robot interaction quality.
Contribution
The paper introduces Affecta-Context, a novel framework that clusters physical contexts and learns behavior prioritization through human interactions, enhancing robot adaptability.
Findings
The framework successfully learned behavior prioritization over 72 interactions.
It demonstrated the ability to generalize to new, unseen physical contexts.
The approach improved the robot's behavior alignment with user preferences.
Abstract
This paper presents Affecta-context, a general framework to facilitate behavior adaptation for social robots. The framework uses information about the physical context to guide its behaviors in human-robot interactions. It consists of two parts: one that represents encountered contexts and one that learns to prioritize between behaviors through human-robot interactions. As physical contexts are encountered the framework clusters them by their measured physical properties. In each context, the framework learns to prioritize between behaviors to optimize the physical attributes of the robot's behavior in line with its current environment and the preferences of the users it interacts with. This paper illlustrates the abilities of the Affecta-context framework by enabling a robot to autonomously learn the prioritization of discrete behaviors. This was achieved by training across 72…
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