A key research area is the integration of machine perception with planning and action for robotics.
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.