  We present a framework for learning to guide geometric task and motion
planning (GTAMP). GTAMP is a subclass of task and motion planning in which the
goal is to move multiple objects to target regions among movable obstacles. A
standard graph search algorithm is not directly applicable, because GTAMP
problems involve hybrid search spaces and expensive action feasibility checks.
To handle this, we introduce a novel planner that extends basic heuristic
search with random sampling and a heuristic function that prioritizes
feasibility checking on promising state action pairs. The main drawback of such
pure planners is that they lack the ability to learn from planning experience
to improve their efficiency. We propose two learning algorithms to address
this. The first is an algorithm for learning a rank function that guides the
discrete task level search, and the second is an algorithm for learning a
sampler that guides the continuous motionlevel search. We propose design
principles for designing data efficient algorithms for learning from planning
experience and representations for effective generalization. We evaluate our
framework in challenging GTAMP problems, and show that we can improve both
planning and data efficiency
