High-order neural networks for the learning of robot contact surface shape

Author

Kosmatopoulos, Elias B. ; Christodoulou, Manolis A.

Author_Institution

Dept. of Electr. Eng. Syst., Univ. of Southern California, Los Angeles, CA, USA

Volume

13

Issue

3

fYear

1997

fDate

6/1/1997 12:00:00 AM

Firstpage

451

Lastpage

455

Abstract

It is known that the problem of learning the shape parameters of unknown surfaces that are in contact with a robot end-effector can be formulated as a nonlinear parameter estimation problem and an extended Kalman filter can be applied in order to estimate the surface shape parameters. In this paper, we show that the problem of learning the shape parameters of unknown contact surfaces can be formulated as a linear parameter estimation problem and thus globally convergent learning laws can be applied. Moreover, we show that by using appropriate neural network approximators, the unknown surfaces can be learned even if there are no force measurements, i.e., the robot is not provided with any force or tactile sensors

Keywords

learning (artificial intelligence); manipulators; neural nets; observers; parameter estimation; state estimation; globally convergent learning laws; high-order neural networks; learning; linear parameter estimation problem; neural network approximators; robot contact surface shape; robot end-effector; shape parameters; unknown surfaces; Algorithm design and analysis; Convergence; Force measurement; Force sensors; Manipulators; Neural networks; Parameter estimation; Robot sensing systems; Shape measurement; Tactile sensors;

fLanguage

English

Journal_Title

Robotics and Automation, IEEE Transactions on

Publisher

ieee

ISSN

1042-296X

Type

jour

DOI

10.1109/70.585906

Filename

585906