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Learning an Internal Dynamics Model from Control Demonstration.

Matthew D Golub1, Steven M Chase1, Byron M Yu1

  • 1Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213 USA.

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Summary
This summary is machine-generated.

This study reveals that subjects, like nonhuman primates in brain-machine interface (BMI) control, develop internal models of dynamics that differ from actual plant dynamics, impacting control accuracy.

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Area of Science:

  • Neuroscience
  • Control Theory
  • Machine Learning

Background:

  • Optimal control often assumes perfect knowledge of plant dynamics.
  • Human and animal controllers may possess internal models that diverge from true plant dynamics.

Purpose of the Study:

  • To develop a method for learning a subject's internal model from control demonstrations.
  • To account for sensory feedback delays and internal state predictions in control systems.

Main Methods:

  • A probabilistic framework and expectation-maximization (EM) algorithm were developed.
  • The framework jointly estimates the internal model, internal state trajectories, and feedback delay.
  • Applied to brain-machine interface (BMI) control data from a nonhuman primate.

Main Results:

  • The subject's internal model of BMI plant dynamics was estimated.
  • This internal model deviated from the true plant dynamics.
  • The subject's internal model better explained recorded neural control signals than the true plant dynamics.

Conclusions:

  • Internal models used by controllers can differ significantly from actual system dynamics.
  • Accurate modeling of internal dynamics is crucial for understanding and improving biological and artificial control systems.
  • This framework offers a novel approach to inferring internal models and feedback delays in control tasks.