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Modeling human target reaching with an adaptive observer implemented with dynamic neural fields.

Farzaneh S Fard1, Paul Hollensen1, Dietmar Heinke2

  • 1Faculty of Computer Science, Dalhousie University, NS, Canada.

Neural Networks : the Official Journal of the International Neural Network Society
|November 13, 2015
PubMed
Summary

This study introduces a biologically inspired robotic arm controller using a neural field framework. The system demonstrates accurate movement and compensates for sensory delays, mimicking human motor control.

Keywords:
Adaptive controllerDynamic neural fieldsInternal modelObserverPath integrationTarget reaching

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Area of Science:

  • Robotics
  • Computational Neuroscience
  • Biophysics

Background:

  • Human motor control exhibits remarkable accuracy despite sensory feedback delays or absence.
  • Arm dynamics can change due to development or injury, necessitating adaptive control mechanisms.

Purpose of the Study:

  • To develop a biologically motivated robotic arm controller using a neural field framework.
  • To implement an adaptive observer for robust path integration and compensation of sensory delays.

Main Methods:

  • Utilized a dynamic neural field framework for implementing an adaptive observer.
  • Trained a path integration mechanism from limited examples.
  • Adapted motor effect strength to implicitly compensate for image acquisition delays.

Main Results:

  • Demonstrated successful generalization of path integration for robotic arm movement in arbitrary directions and velocities.
  • The adaptive observer successfully guided the robotic arm in the dark.
  • The model's movements exhibited a bell-shaped velocity profile, consistent with human behavior.

Conclusions:

  • The proposed dynamic neural field implementation provides a robust and adaptive robotic arm controller.
  • The model successfully replicates key aspects of human motor control, including path integration and velocity profiles.
  • This approach offers a promising framework for understanding and replicating biological motor control in artificial systems.