A new system architecture for applying symbolic learning techniques to robot manipulation tasks

Author

Hauser, M.

Volume

2

fYear

1993

fDate

26-30 Jul 1993

Firstpage

1441

Abstract

Presents a new system architecture that enables a robot control system to learn at a symbolic level during planning and executing tasks. A user can program the robot by simply demonstrating the tasks it should perform. By performing inductive generalization and specialization steps, the system is able to improve its knowledge base. The architecture consists of a set of distributed knowledge units which realize a focus of attention that is necessary for efficient execution and learning, and which also makes competition between different problem solutions possible

Keywords

manipulators; competition; demonstrating; distributed knowledge units; executing tasks; focus of attention; inductive generalization; inductive specialization; knowledge base; planning; problem solutions; robot manipulation tasks; robot programming; symbolic learning techniques; system architecture; Computer architecture; Control systems; Educational robots; Informatics; Problem-solving; Production systems; Robot control; Robot programming; Robotics and automation; Telerobotics;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Robots and Systems '93, IROS '93. Proceedings of the 1993 IEEE/RSJ International Conference on

Conference_Location

Yokohama

Print_ISBN

0-7803-0823-9

Type

conf

DOI

10.1109/IROS.1993.583813

Filename

583813