Title:
Transferring Embodied Concepts Between Perceptually Heterogeneous Robots
Transferring Embodied Concepts Between Perceptually Heterogeneous Robots
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Kira, Zsolt
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Abstract
This paper explores methods and representations
that allow two perceptually heterogeneous robots, each of
which represents concepts via grounded properties, to transfer
knowledge despite their differences. This is an important issue,
as it will be increasingly important for robots to communicate
and effectively share knowledge to speed up learning as they
become more ubiquitous.We use Gӓrdenfors’ conceptual spaces
to represent objects as a fuzzy combination of properties such as
color and texture, where properties themselves are represented
as Gaussian Mixture Models in a metric space. We then 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. These mappings are then
used to transfer a concept from one robot to another, where
the receiving robot was not previously trained on instances
of the objects. We show in a 3D simulation environment that
these models can be successfully learned and concepts can be
transferred between a ground robot and an aerial quadrotor
robot.
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Date Issued
2009
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Paper