One of the long term objectives of robotics and
artificial cognitive systems is that robots will increasingly be
capable of interacting in a cooperative and adaptive manner
with their human counterparts in open-ended tasks that can
change in real-time. In such situations, an important aspect of
the robot behavior will be the ability to acquire new knowledge
of the cooperative tasks by observing humans. At least two
significant challenges can be identified in this context. The first
challenge concerns development of methods to allow the
characterization of human actions such that robotic systems can
observe and learn new actions, and more complex behaviors
made up of those actions. The second challenge is associated
with the immense heterogeneity and diversity of robots and
their perceptual and motor systems. The associated question is
whether the identified methods for action perception can be
generalized across the different perceptual systems inherent to
distinct robot platforms. The current research addresses these
two challenges. We present results from a cooperative human-
robot interaction system that has been specifically developed for
portability between different humanoid platforms. Within this
architecture, the physical details of the perceptual system (e.g.
video camera vs IR video with reflecting markers) are
encapsulated at the lowest level. Actions are then automatically
characterized in terms of perceptual primitives related to
motion, contact and visibility.
The resulting system is
demonstrated to perform robust object and action learning and
recognition on two distinct robotic platforms. Perhaps most
interestingly, we demonstrate that knowledge acquired about
action recognition with one robot can be directly imported and
successfully used on a second distinct robot platform for action
recognition. This will have interesting implications for the
accumulation of shared knowledge between distinct
heterogeneous robotic systems.
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