Learning complex tasks using a stepwise approach

doi:10.1023/A:1008058131402

Learning complex tasks using a stepwise approach

1999-01-01

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DOI：10.1023/A:1008058131402

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This paper explores a stepwise learning approach based on a system's decomposition into functional subsystems. Two case studies are examined: a visually guided robot that learns to track a maneuvering object, and a robot that learns to use the information from a force sensor in order to put a peg into a hole. These two applications show the features and advantages of the proposed approach: i) the subsystems naturally arise as functional components of the hardware and software; ii) these subsystems are building blocks of the robot behavior and can be combined in several ways for performing various tasks; iii) this decomposition makes it easier to check the performances and detect the cause of a malfunction; iv) only those subsystems for which a satisfactory solution is not available need to be learned; v) the strategy proposed for coordinating the optimization of all subsystems ensures an improvement at the task-level; vi) the overall system's behavior is significantly improved by the stepwise learning approach.

Keyword:

learning robots

system organization

optimization

physical equation

look-ut table

neural networks

fuzzy controllers

Learning complex tasks using a stepwise approach

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