Abstract
Learning from demonstration presents an alternative
method for programming robots for different nontrivial
behaviors. Various techniques that address learning
from demonstration in robots have been proposed but
those do not scale up well. Thus there is a need to
discover novel solutions to this problem. Given that
the basic idea for such learning comes from nature in
the form of imitation in few animals, it makes perfect
sense to take advantage of the rigorous study of
imitative learning available in relevant natural
sciences. In this work a solution for robot learning
from a relatively recent theory from natural sciences
called the Shared Circuits Model, is sought. Shared
Circuits Model theory is a comprehensive,
multidiscipline representative theory. It is a modern
synthesis that brings together different theories that
explain imitation and other related social functions
originating from various sciences. This paper attempts
to import the shared circuits model to robotics for
learning from demonstration. Specifically it: (1)
expresses shared circuits model in a software design
nomenclature; (2) heuristically extends the basic
specification of Shared Circuits Model to implement a
working imitative learning system; (3) applies the
extended model on mobile robot navigation in a
simulated indoor environment; and (4) attempts to
validate the shared circuits model theory in the
context of imitative learning. Results show that an
extremely simple implementation of a theoretically
sound theory, the shared circuits model, offers a
realistic solution for robot learning from
demonstration of nontrivial tasks.
Users
Please
log in to take part in the discussion (add own reviews or comments).