Much work in recent years has focused on transferring human skill
to robots by abstracting that skill into a machine-understandable,
computational model. Such skill models, however, can be used not only
for transferring human control strategy to robots, but also for
helping less-skilled human operators improve their performance. We
propose a two-step approach for transferring skill from human expert
to human apprentice. An expert's relevant control strategies or skills
are first abstracted into a sensory-based computational
model. Afterwards, this trained computational model is used to
generate on-line advice for less-skilled operators who need to improve
their skill. This advice can take advantage of many different sensor
modalities, thereby potentially improving both the quality and speed
of learning for the apprentice. Furthermore, this approach allows for
the efficient transfer of skill from a single expert to many
apprentices, as well as from many experts to a single apprentice. In
this paper, we first describe a flexible neural-network-based method
for modeling human control strategy and provide motivation for its
use. We then present a case study for teaching control strategy from
one person to another in this two-step approach of transferring
skill.
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