AVAILABLE IN NEUROPROSE: "Learning control under extreme uncertainty"

[vijay@envy.cs.umass.edu]

The following paper has been placed in the Neuroprose archive.
Thanks to Jordan Pollack for providing this service. Comments
and questions are welcome.

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	Learning Control Under Extreme Uncertainty

		   Vijaykumar Gullapalli
		Computer Science Department
		University of Massachusetts
		     Amherst, MA 01003
				     

			 Abstract

A peg-in-hole insertion task is used as an example to illustrate
the utility of direct associative reinforcement learning methods
for learning control under real-world conditions of uncertainty
and noise.  Task complexity due to the use of an unchamfered
hole and a clearance of less than $0.2mm$ is compounded by the
presence of positional uncertainty of magnitude exceeding $10$
to $50$ times the clearance.  Despite this extreme degree of
uncertainty, our results indicate that direct reinforcement
learning can be used to learn a robust reactive control strategy
that results in skillful peg-in-hole insertions.

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FTP INSTRUCTIONS 

     unix% Getps gullapalli.uncertainty-nips5.ps.Z

if you have the shell script, or

     unix% ftp archive.cis.ohio-state.edu (or 128.146.8.52)
     Name: anonymous
     Password: neuron
     ftp> cd pub/neuroprose
     ftp> binary
     ftp> get gullapalli.uncertainty-nips5.ps.Z
     ftp> bye
     unix% zcat gullapalli.uncertainty-nips5.ps.Z | lpr