The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

The central thesis of this article is that memory-based methods provide natural and powerful mechanisms for high-autonomy learning control. This paper takes the form of a survey of the ways in which memory-based methods can and have been applied to control tasks, with an emphasis on tasks in robotics and manufacturing. We explain the various forms that control tasks can take, and how this impacts on the choice of learning algorithm. We show a progression of five increasingly more complex algorithms which are applicable to increasingly more complex kinds of control tasks. We examine their empirical behavior on robotic and industrial tasks. The final section discusses the interesting impact that explicitly remembering all previous experiences has on the problem of learning control.

Notes

Grant ID: F33615-93-1-1330

Associated Lab(s) / Group(s): Auton Lab

Associated Project(s): Auton Project

Number of pages: 39

Text Reference

Andrew Moore, C. G. Atkeson, and S. A. Schaal, "Memory-Based Learning for Control," tech. report CMU-RI-TR-95-18, Robotics Institute, Carnegie Mellon University, April, 1995

BibTeX Reference

@techreport{Moore_1995_377,
   author = "Andrew Moore and C. G. Atkeson and S. A. Schaal",
   title = "Memory-Based Learning for Control",
   booktitle = "",
   institution = "Robotics Institute",
   month = "April",
   year = "1995",
   number= "CMU-RI-TR-95-18",
   address= "Pittsburgh, PA",
}

The Robotics Institute

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