Task-level robot learning

6 January 2003

conference paper
Published by Institute of Electrical and Electronics Engineers (IEEE)

p. 1309-1310
https://doi.org/10.1109/robot.1988.12245

Abstract

The functionality of robots can be improved by programming them to learn tasks from practice. Task-level learning can compensate for the structural modeling errors of the robot's lower-level control systems and can speed up the learning process by reducing the degrees of freedom of the models to be learned. The authors demonstrate two general learning procedures-fixed-model learning and refined-model learning-on a ball-throwing robot system. Both learning approaches refine the task command based on the performance error of the system, while they ignore the intermediate variables separation the lower-level systems. The authors also provide experimental and theoretical evidence that task-level learning can improve the functionality of robots.

Keywords

This publication has 2 references indexed in Scilit:

Task-level robot learning
Published by Institute of Electrical and Electronics Engineers (IEEE) ,2003
Direct-Drive Robots
Published by MIT Press ,1987