Accession Number : ADA297446

Title :   Teaching Accommodation Task Skills: From Human Demonstration to Robot Control via Artificial Neural Networks.

Descriptive Note : Doctoral thesis,

Corporate Author : AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH

Personal Author(s) : Whalen, Paul V.

PDF Url : ADA297446

Report Date : MAR 1995

Pagination or Media Count : 282

Abstract : A simple edge-mating task, performed automatically by accommodation control, was used to study the feasibility of using data collected during a human demonstration to train an artificial neural network (ANN) to control a common robot manipulator to complete similar tasks. The 2-dimensional (planar) edge-mating task which aligns a peg normal to a fiat table served as the basis for the investigation. A simple multi-layered perceptron (MLP) ANN with a single hidden layer and linear output nodes was tralned using the back-propagation algorithm with momentum. The inputs to the ANN were the planar components of the contact force between the peg and the table. The outputs from the ANN were the planar components of a commanded velocity. The controller was architected so the ANN could learn a configuration-independent solution by operating in the tool-frame coordinates. As a baseline of performance, a simple accommodation matrix capable of completing the edge- mating task was determined and implemented in simulation and on the PUMA manipulator. The accommodation matrix was also used to synthesize various forms of training data which were used to galn insights into the function and vulnerabilities of the proposed control scheme. Human demonstration data were collected using a gravity-compensated PUMA 562 manipulator and using a custom-built planar, low-impedance motion measurement system (PLIMMS). (KAR) P. 31

Descriptors :   *NEURAL NETS, *SKILLS, *ROBOTS, *COMPUTER AIDED INSTRUCTION, COMPUTERIZED SIMULATION, LINEAR SYSTEMS, OUTPUT, CONTROL, MEASUREMENT, TRAINING, DEMONSTRATIONS, HUMANS, LAYERS, MOTION, NODES, BASE LINES, FEASIBILITY STUDIES, PLANAR STRUCTURES, LOW LEVEL, DATA ACQUISITION, IMPEDANCE, MANIPULATORS, MOMENTUM.

Subject Categories : Computer Programming and Software
      Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE