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|Title:||Explanation-Based Learning of Generalized Robot Assembly Plans|
|Author(s):||Segre, Alberto Maria|
|Department / Program:||Electrical Engineering|
|Degree Granting Institution:||University of Illinois at Urbana-Champaign|
|Abstract:||This thesis describes an experiment involving the application of a recently developed machine learning technique, explanation-based learning, to the robot retraining problem. Explanation-based learning permits a system to acquire generalized problem-solving knowledge on the basis of a single observed problem-solving example. The resulting computer program, called ARMS for Acquiring Robotic Manufacturing Schemata, serves as a medium for discussing issues related to this particular type of learning. This work clarifies and extends the corpus of knowledge so that explanation-based learning can be successfully applied to real-world problems.
From a machine learning perspective, ARMS is one of the more ambitious working explanation-based learning implementations to date. Unlike many other vehicles for machine-learning research, the ARMS system operates in a non-trivial domain conveying the flavor of a real robot assembly application.
From a robotics perspective, ARMS represents an important first step towards a learning-apprentice system for manufacturing. It posits a theoretically more satisfactory solution to the robot retraining problem, and offers an eventual alternative to the limitations of robot programming.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1987.
|Date Available in IDEALS:||2014-12-15|
This item appears in the following Collection(s)
Dissertations and Theses - Electrical and Computer Engineering
Dissertations and Theses in Electrical and Computer Engineering
Graduate Dissertations and Theses at Illinois
Graduate Theses and Dissertations at Illinois