Using Backpropagation to Learn the Dynamics of a Real Robot Arm

Abstract

Computing the inverse dynamics of a robot arm is an active area of research in the control literature. We apply a back propagation network to this problem and measure its performance on the CMU Direct-Drive Arm II for a family of pick and place trajectories. Trained on a random sample of these trajectories, the network is shown to generalize top new samples drawn from the same family. The weights developed during the learning phase are reminiscent of the velocity and acceleration filters used in standard control theory. Keywords: Robotics; Manipulators; Neural networks; Robot control; Learning manipulator dynamics; Artificial intelligence.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1988
Accession Number
ADA219111

Entities

People

  • Barak A. Pearlmutter
  • Ken Goldberg

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Computational Complexity
  • Computational Science
  • Computations
  • Computer Science
  • Computers
  • Data Storage Systems
  • Errors
  • Identification
  • Joints
  • Joints (Anatomy)
  • Learning
  • Manipulators
  • Neural Networks
  • Signal Processing
  • Transfer Functions

Readers

  • Neural Network Machine Learning.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Autonomy