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Learning Dextrous Manipulation Skills for Multifingered Robot Hands |
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@TechReport{Fuentes:TR96,
author = {Olac Fuentes and Randal C. Nelson},
title = {Learning Dextrous Manipulation Skills for Multifingered Robot Hands},
institution = {Comp Sci Dept University of Rochester},
year = {1996},
number = {613},
address = {Rochester NY},
month = {May},
abstract = {
We present a method for autonomous learning of dextrous manipulation skills with multifingered
robot hands. We use heuristics derived from observations made on human hands to reduce the
degrees of freedom of the task and make learning tractable. Our approach consists of learning
and storing a few basic manipulation primitives for a few prototypical objects and then using
an associative memory to obtain the required parameters for new objects and/or manipulations.
The parameter space of the robot is searched using a modified version of the evolution strategy,
which is robust to the noise normally present in real-world complex robotic tasks. Given the
difficulty of modeling and simulating accurately the interactions of multiple fingers and an
object, and to ensure that the learned skills are applicable in the real world, our system does
not rely on simulation; all the experimentation is performed by a physical robot, in this case
the 16-degree-of-freedom Utah/MIT hand. Experimental results show that accurate dextrous
manipulation skills can be learned by the robot in a short period of time. }
}