Model-Based Robot Learning

Abstract

Models play an important role in learning from practice. Models of a controlled system can be used as learning operators to refine commands on the basis of performance errors. The examples used to demonstrate this include positioning a limb at a visual target, and following a defined trajectory. Better models lead to faster correction of command errors, requiring less practice to attain a given level of performance. The benefits of accurate modeling are improved performance in all aspects of control, while the risks of inadequate modeling are poor learning performance, or even degradation of performance with practice.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1988
Accession Number
ADA208265

Entities

People

  • Christopher G. Atkeson
  • David J. Reinkensmeyer
  • Eric W. Aboaf
  • Joseph Mcintyre

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Biomedical

DTIC Thesaurus Topics

  • Adaptive Systems
  • Algorithms
  • Artificial Intelligence
  • Automation
  • Cognitive Science
  • Control
  • Control Systems
  • Control Systems Engineering
  • Control Theory
  • Convergence
  • Dynamics
  • Eigenvalues
  • Equations
  • Filters
  • Military Research
  • Universities
  • Visual Targets

Readers

  • Computational Modeling and Simulation
  • Robotics and Automation.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks
  • Autonomy