  In this paper, we examine the problem of rearranging many objects on a
tabletop in a cluttered setting using overhand grasps. Efficient solutions for
the problem, which capture a common task that we solve on a daily basis, are
essential in enabling truly intelligent robotic manipulation. In a given
instance, objects may need to be placed at temporary positions ("buffers") to
complete the rearrangement, but allocating these buffer locations can be highly
challenging in a cluttered environment. To tackle the challenge, a two-step
baseline planner is first developed, which generates a primitive plan based on
inherent combinatorial constraints induced by start and goal poses of the
objects and then selects buffer locations assisted by the primitive plan. We
then employ the "lazy" planner in a tree search framework which is further sped
up by adapting a novel preprocessing routine. Simulation experiments show our
methods can quickly generate high-quality solutions and are more robust in
solving large-scale instances than existing state-of-the-art approaches.
  source:github.com/arc-l/TRLB
