Evolutionary, developmental neural networks for robust robotic control
Name
132697453-MIT.pdf
Description
Full printable version
Size
13.2 MB
Format
Adobe PDF
Checksum (MD5)
4d6308069833b800ef3b8e75d1a047c1
Author(s)
Adams, Bryan (Bryan Paul), 1977-
Advisor(s)
Rodney A. Brooks.
Date Issued
2006
Publisher
Massachusetts Institute of Technology
Abstract
The use of artificial evolution to synthesize controllers for physical robots is still in its infancy. Most applications are on very simple robots in artificial environments, and even these examples struggle to span the "reality gap," a name given to the difference between the performance of a simulated robot and the performance of a.real robot using the same evolved controller. This dissertation describes three methods for improving the use of artificial evolution as a tool for generating controllers for physical robots. First, the evolutionary process must incorporate testing on the physical robot. Second, repeated structure on the robot should be exploited. Finally, prior knowledge about the robot and task should be meaningfully incorporated. The impact of these three methods, both in simulation and on physical robots, is demonstrated, quantified, and compared to hand-designed controllers.
Description
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
Includes bibliographical references (p. 136-143).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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