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Related Concept Videos

Self-Schemas02:16

Self-Schemas

In general, a schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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Related Experiment Video

Updated: May 10, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Semantic framework for social robot self-configuration.

Gorka Azkune1, Pablo Orduña, Xabier Laiseca

  • 1Tecnalia-Industry and Transport Division, Donostia, Spain. gorka.azcune@tecnalia.com

Sensors (Basel, Switzerland)
|June 14, 2013
PubMed
Summary
This summary is machine-generated.

Social robots in healthcare can adapt to unpredictable situations using a novel self-configuration approach. This system leverages a semantic framework and ontology for intelligent decision-making, enhancing robot adaptability.

Related Experiment Videos

Last Updated: May 10, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Robot Interaction

Background:

  • Healthcare environments are dynamic and unpredictable, posing challenges for social robot deployment.
  • Existing social robots often struggle to adapt to unforeseen circumstances in real-world settings.

Purpose of the Study:

  • To present a robot self-configuration approach for enhancing adaptability in unpredictable environments.
  • To enable social robots to manage changing situations effectively through intelligent decision-making.

Main Methods:

  • Integration of a semantic framework with a reasoner for dynamic service and resource configuration.
  • Development of an ontology to model the robot and relevant contextual information.
  • Utilization of rule-based systems to encode human knowledge as policies for the reasoner.

Main Results:

  • Successful implementation of the self-configuration approach in a mobile robot.
  • Demonstrated increased capability of the robot in handling previously unencountered situations.
  • The robot showed improved performance in adapting to novel environmental challenges.

Conclusions:

  • The proposed self-configuration approach significantly enhances social robot adaptability in complex environments.
  • Semantic frameworks and ontologies are effective tools for enabling intelligent robot decision-making and context awareness.
  • This research paves the way for more robust and versatile social robots in healthcare and other dynamic fields.