Seminar on Computational Learning and
Adaptation
Towards Learning Object Models of Physical Spaces with Mobile Robots
Sebastian Thrun
Carnegie Mellon University
University of Pittsburgh
Stanford University
thrun@Stanford.edu
While much of machine learning takes place in virtual spaces, learning models
of physical spaces is a fascinating and highly active research area. Recent
research on robotic map learning has led to a flurry of statistical algorithms
for inferring models from sensor measurements. However, the vast majority of
work learns models of static environments, at the pixel level. The work
presented in this talk seeks to acquire object models, where objects might be
movable items (chairs, doors, people?), or structural primitives (walls). The
tool of choice for learning such object models is the expectation maximization
algorithm, capable of learning model identity, pose and appearance parameters
with latent data association problems. Come and enjoy an introduction into a
body of work on environment modeling, along with videos of roaming robots.
This is joint work with Dragomir Anguelov, Rahul Biswas, Daphne Koller, Benson
Limketkai, Christian Martin, Michael Montemerlo, and Scott Sanner.
Date: Thursday, March 14
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Time: 4:15-5:30PM
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Place: Cordura 100
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