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Task-Level Robot Learning

Author(s)
Aboaf, Eric W.
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Abstract
We are investigating how to program robots so that they learn from experience. Our goal is to develop principled methods of learning that can improve a robot's performance of a wide range of dynamic tasks. We have developed task-level learning that successfully improves a robot's performance of two complex tasks, ball-throwing and juggling. With task- level learning, a robot practices a task, monitors its own performance, and uses that experience to adjust its task-level commands. This learning method serves to complement other approaches, such as model calibration, for improving robot performance.
Date issued
1988-08-01
URI
http://hdl.handle.net/1721.1/6972
Other identifiers
AITR-1079
Series/Report no.
AITR-1079

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  • AI Technical Reports (1964 - 2004)

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