  In this paper, we propose a learning algorithm that speeds up the search in
task and motion planning problems. Our algorithm proposes solutions to three
different challenges that arise in learning to improve planning efficiency:
what to predict, how to represent a planning problem instance, and how to
transfer knowledge from one problem instance to another. We propose a method
that predicts constraints on the search space based on a generic representation
of a planning problem instance, called score-space, where we represent a
problem instance in terms of the performance of a set of solutions attempted so
far. Using this representation, we transfer knowledge, in the form of
constraints, from previous problems based on the similarity in score space. We
design a sequential algorithm that efficiently predicts these constraints, and
evaluate it in three different challenging task and motion planning problems.
Results indicate that our approach performs orders of magnitudes faster than an
unguided planner
