Imitation learning of motor primitives and language bootstrapping in
  robots
  Imitation learning in robots, also called programing by demonstration, has
made important advances in recent years, allowing humans to teach context
dependant motor skills/tasks to robots. We propose to extend the usual contexts
investigated to also include acoustic linguistic expressions that might denote
a given motor skill, and thus we target joint learning of the motor skills and
their potential acoustic linguistic name. In addition to this, a modification
of a class of existing algorithms within the imitation learning framework is
made so that they can handle the unlabeled demonstration of several tasks/motor
primitives without having to inform the imitator of what task is being
demonstrated or what the number of tasks are, which is a necessity for language
learning, i.e; if one wants to teach naturally an open number of new motor
skills together with their acoustic names. Finally, a mechanism for detecting
whether or not linguistic input is relevant to the task is also proposed, and
our architecture also allows the robot to find the right framing for a given
identified motor primitive. With these additions it becomes possible to build
an imitator that bridges the gap between imitation learning and language
learning by being able to learn linguistic expressions using methods from the
imitation learning community. In this sense the imitator can learn a word by
guessing whether a certain speech pattern present in the context means that a
specific task is to be executed. The imitator is however not assumed to know
that speech is relevant and has to figure this out on its own by looking at the
demonstrations: indeed, the architecture allows the robot to transparently also
learn tasks which should not be triggered by an acoustic word, but for example
by the color or position of an object or a gesture made by someone in the
environment. To demonstrate this ability to find the ...
