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A neurorobotics approach to behaviour selection based on human activity recognition.

Caetano M Ranieri1, Renan C Moioli2, Patricia A Vargas3

  • 1Institute of Mathematical and Computer Sciences, University of Sao Paulo, Avenida Trabalhador Sao Carlense, 400, Sao Carlos, SP 13566-590 Brazil.

Cognitive Neurodynamics
|July 31, 2023
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Summary
This summary is machine-generated.

This study introduces a neurorobotics model for robots to select behaviors effectively during human-robot interaction. The model integrates brain circuit simulations with human activity recognition, improving autonomous robot responses.

Keywords:
Behaviour selectionBioinspired computational modelHuman activity recognitionNeuroroboticsRobot simulation

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Area of Science:

  • Robotics
  • Neuroscience
  • Human-Robot Interaction

Background:

  • Effective human-robot interaction relies on robust human activity recognition and robot behavior selection.
  • Current methods often use deterministic links, ignoring real-time prediction uncertainties.
  • Autonomous robots need sophisticated decision-making capabilities for seamless interaction.

Purpose of the Study:

  • To present an initial neurorobotics model for robot behavior selection.
  • To integrate computational models of the basal ganglia-thalamus-cortex (BG-T-C) circuit with human activity recognition.
  • To address the limitations of deterministic behavior selection in human-robot interaction.

Main Methods:

  • Developed a neurorobotics model simulating mammalian brain circuits (BG-T-C).
  • Coupled the model with advanced human activity recognition techniques.
  • Utilized a robotics simulation environment with a mobile robot in an intelligent home setting.

Main Results:

  • The neurorobotics model demonstrated advantageous behavior selection capabilities.
  • The model effectively linked human activity recognition to robot actions.
  • Performance improved with more accurate activity recognition and complex animal models.

Conclusions:

  • The proposed neurorobotics model offers a promising approach for adaptive robot behavior.
  • Integrating neurophysiological models enhances robot autonomy in human-centric environments.
  • This work highlights the potential of biologically inspired AI for advanced robotics.