Learning failure recovery knowledge for mechanical assembly

Abstract

A framework for planning and supervision of robotized assembly tasks is initially presented, with emphasis on failure recovery. The approach to the integration 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.