  We propose an approach based on probabilistic models, in particular POMDPs,
to plan optimized search processes of known objects by intelligent eye in hand
robotic arms. Searching and reaching for a known object (a pen, a book, or a
hammer) in one's office is an operation that humans perform frequently in their
daily activities. There is no reason why intelligent robotic arms would not
encounter this problem frequently in the various applications in which they are
expected to serve.
  The problem suffers from uncertainties coming both from the lack of
information about the position of the object, from noisy sensors, imperfect
models of the target object, of imperfect models of the environment, and from
approximations in computations. The use of probabilistic models helps us to
mitigate at least a few of these challenges, approaching optimality for this
important task.
