Techreport,
High Dimension Action Spaces in Robot Skill Learning
TR458. University of Rochester, Computer Science Department, (May 1993)
Abstract
Table lookup with interpolation is used for many learning and adaptation
tasks. Redundant mappings capture the important concept of ``motor
skill,'' which is important in real, behaving systems. Few, if any,
robot skill implementations have dealt with redundant mappings, in
which the space to be searched to create the table has much higher
dimensionality than the table itself. A practical method for inverting
redundant mappings is important in physical systems with limited
time for trials. We present the ``Guided Table Fill In'' algorithm,
which uses data already stored in the table to guide search through
the space of potential table entries. The algorithm is illustrated
and tested on a robot skill learning task both in simulation and
on a robot with a flexible link. Our experiments show that the ability
to search high dimensional action spaces efficiently allows skill
learners to find new behaviors that are qualitatively different from
what they were presented or what the system designer may have expected.
Thus the use of this technique can allow researchers to seek higher
dimensional action spaces for their systems rather than constraining
their search space at the risk of excluding the best actions.
