Joseph Modayil and Benjamin Kuipers. 2004.
Bootstrap learning for object discovery.
IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS-04),
We show how a robot can autonomously learn an ontology of
objects to explain many 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 learn stable perceptual
representations of objects. Our proposed unsupervised learning method
uses the properties of 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.