Title:
Mapping Grounded Object Properties Across Perceptually Heterogeneous Embodiments
Mapping Grounded Object Properties Across Perceptually Heterogeneous Embodiments
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Kira, Zsolt
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Abstract
As robots become more common, it becomes increasingly
useful for them to communicate and effectively share
knowledge that they have learned through their individual
experiences. Learning from experiences, however, is oftentimes
embodiment-specific; that is, the knowledge learned is
grounded in the robot’s unique sensors and actuators. This
type of learning raises questions as to how communication
and knowledge exchange via social interaction can occur, as
properties of the world can be grounded differently in
different robots. This is especially true when the robots are
heterogeneous, with different sensors and perceptual
features used to define the properties. In this paper, we
present methods and representations that allow
heterogeneous robots to learn grounded property
representations, such as that of color categories, and then
build models of their similarities and differences in order to
map their respective representations. We use a conceptual
space representation, where object properties are learned
and represented as regions in a metric space, implemented
via supervised learning of Gaussian Mixture Models. We
then propose to use confusion matrices that are built using
instances from each robot, obtained in a shared context, in
order to learn mappings between the properties of each
robot. Results are demonstrated using two perceptually
heterogeneous Pioneer robots, one with a web camera and
another with a camcorder.
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Date Issued
2009
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Paper