publication . Conference object . 2019

Eager to Learn vs. Quick to Complain? How a socially adaptive robot architecture performs with different robot personalities

Ana Tanevska; Francesco Rea; Giulio Sandini; Lola Cañamero; Alessandra Sciutti;
Open Access
  • Published: 29 Nov 2019
  • Publisher: IEEE
A social robot that is aware of our needs and continuously adapts its behaviour to them has the potential of creating a complex, personalized, human-like interaction of the kind we are used to have with our peers in our everyday lives. We are interested in exploring how would an adaptive architecture function and personalize to different users when given different initial values of its variables, i.e. when implementing the same adaptive framework with different robot personalities. Would an architecture that learns very quickly outperform a slower but steadier learning profile? To further explore this, we propose a cognitive architecture for the humanoid robot iCub supporting adaptability and we attempt to validate its functionality and test different robot profiles.
Persistent Identifiers
free text keywords: Social robots and social learning, Human-human and human-robot interaction and communication, Architectures for Cognitive Development and Open-Ended Learning, Humanoid robot, Human–computer interaction, Computer science, Robot, Adaptive architecture, iCub, Cognitive architecture, Social robot
Funded by
investigating Human Shared PErception with Robots
  • Funder: European Commission (EC)
  • Project Code: 804388
  • Funding stream: H2020 | ERC | ERC-STG
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Open Access
Conference object . 2019
Providers: ZENODO
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