Joseph Modayil and Benjamin Kuipers. 2004.
Towards Bootstrap Learning for Object Discovery.
AAAI-2004 Workshop on Anchoring Symbols to Sensor Data.
Abstract
We show how a robot can autonomously learn an ontology of objects to
explain aspects of its sensor input from an unknown dynamic
world. Unsupervised learning about objects is an important conceptual
step in developmental learning, whereby the agent clusters
observations across space and time to construct stable perceptual
representations of objects. Our proposed unsupervised learning method
uses the properties of allocentric occupancy grids to classify
individual sensor readings as static or dynamic. Dynamic readings are
clustered and the clusters are tracked over time to identify objects,
separating them both from the background of the environment and from
the noise of unexplainable sensor readings. Once trackable clusters
of sensor readings (i.e., objects) have been identified, we build
shape models where they are stable and consistent properties of these
objects. However, the representation can tolerate, represent, and
track amorphous objects as well as those that have well-defined shape.
In the end, the learned ontology makes it possible for the robot to
describe a cluttered dynamic world with symbolic object descriptions
along with a static environment model, both models grounded in sensory
experience, and learned without external supervision.
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