Learning failure recovery knowledge for mechanical assembly

L. Seabra Lopes, L. M. Camarinha-Matos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

A framework for planning and supervision of robotized assembly tasks is initially presented, with emphasis on failure recovery. The approach to the integration of services and the modeling of tasks, resources and environment is briefly described. A planning strategy and domain knowledge for nominal plan execution is presented. Through the use of machine learning techniques, the supervision architecture will be given capabilities for improving its performance over time. In particular, an approach for memorizing failure recovery episodes, based on abstraction, deductive generalization and feature construction, is presented. Recovery planning consists of adapting plan skeletons from similar episodes previously occurred.

Original languageEnglish
Title of host publicationIEEE International Conference on Intelligent Robots and Systems
Editors Anon
PublisherIEEE
Pages712-719
Number of pages8
Volume2
Publication statusPublished - 1996
EventProceedings of the 1996 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS. Part 3 (of 3) - Osaka, Jpn
Duration: 4 Nov 19968 Nov 1996

Conference

ConferenceProceedings of the 1996 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS. Part 3 (of 3)
CityOsaka, Jpn
Period4/11/968/11/96

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Seabra Lopes, L., & Camarinha-Matos, L. M. (1996). Learning failure recovery knowledge for mechanical assembly. In Anon (Ed.), IEEE International Conference on Intelligent Robots and Systems (Vol. 2, pp. 712-719). IEEE.