| dc.description.abstract | Many techniques for speedup learning and
knowledge compilation focus on the learning and
optimization of macro-operators or control rules
in task domains that can be characterized using a
problem-space search paradigm. However, such
a characterization does not fit well the class of
task domains in which the problem solver is required
to perform in a continuous manner. For
example, in many robotic domains, the problem
solver is required to monitor real-valued perceptual
inputs and vary its motor control parameters
in a continuous, on-line manner to successfully
accomplish its task. In such domains, discrete
symbolic states and operators are difficult to define.
To improve its performance in continuous
problem domains, a problem solver must learn,
modify, and use “continuous operators” that continuously
map input sensory information to appropriate
control outputs. Additionally, the problem
solver must learn the contexts in which those
continuous operators are applicable. We propose
a learning method that can compile sensorimotor
experiences into continuous operators, which
can then be used to improve performance of the
problem solver. The method speeds up the task
performance as well as results in improvements in
the quality of the resulting solutions. The method
is implemented in a robotic navigation system,
which is evaluated through extensive experimentation. | en_US |
