Tod Levitt, Senior Research Associate
- Robotics Laboratory
- Department of Computer Science
- Stanford University
- Stanford, CA 94305
-
tlevitt@cs.stanford.edu
Office: Cedar A7, (415) 723-4676
Research Interests
Computer Vision, Robotics, Uncertainty in AI (theories and practice of
belief systems for machine intelligence). A key research area is the
integration of machine perception with planning and action for
robotics. Computer vision is still very limited; good biological and
engineering models yield great uncertainty in scene interpretation.
Bayesian inference is a fundamental approach to guiding robotic
behavior from uncertain perceptions. Recent work involves extensions
of Bayesian inference to hierarchical computer vision, connecting
image processing and pattern recognition evidence with high level
object understanding and manipulation. Binford's quasi-invariant image
and geometric features are the basis for probabilistic estimates of
evidence of scene objects. Hierarchical utility models and extended
influence diagram techniques are being developed to form a physically
deep, but combinatorially tractable, method of guiding robotics
perception, inference and action.
www@flamingo.stanford.edu