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A Mind-inspired Architecture for Adaptive HRI.

Alessandro Umbrico1, Riccardo De Benedictis1, Francesca Fracasso1

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

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Social robots can now offer personalized assistance using a novel dual-process cognitive architecture. This mind-inspired system adapts robot behavior to individual users and social contexts for more engaging interactions.

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Assistant RoboticsEngaging and personalized HRIMind-inspired architectures for social robotsMulti-modal interaction

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Robot Interaction

Background:

  • Social robots require intelligent, adaptive behaviors for continuous user engagement.
  • Current robots often lack personalization, considering individual needs, context, and interaction dynamics.
  • Achieving robust, contextualized robot behavior is a key challenge in human-robot interaction.

Purpose of the Study:

  • To introduce a dual-process cognitive architecture for social robots inspired by human cognition.
  • To enable personalized robot behaviors adaptable to user specificity and social context.
  • To foster empathetic relationships through multi-modal interaction and emotional-cognitive profiling.

Main Methods:

  • Developed a dual-process cognitive architecture with two reasoning layers operating on different timescales.
  • Integrated multi-modal interaction, including verbal and non-verbal expressions.
  • Exemplified the architecture in a cognitive stimulation domain with experimental validation.

Main Results:

  • Demonstrated personalization capabilities of the cognitive architecture.
  • Showcased the joint operation of the dual reasoning layers.
  • Feasibility assessment confirmed customization of robot behaviors and adaptation to user states.

Conclusions:

  • The proposed dual-process cognitive architecture effectively supports personalized and adaptive social robot behavior.
  • The system shows potential for enhancing user interaction and fostering empathetic relationships.
  • Experimental results validate the approach's feasibility in real-world assistance scenarios.