  Prehensile object rearrangement in cluttered and confined spaces has broad
applications but is also challenging. For instance, rearranging products in a
grocery shelf means that the robot cannot directly access all objects and has
limited free space. This is harder than tabletop rearrangement where objects
are easily accessible with top-down grasps, which simplifies robot-object
interactions. This work focuses on problems where such interactions are
critical for completing tasks. It proposes a new efficient and complete solver
under general constraints for monotone instances, which can be solved by moving
each object at most once. The monotone solver reasons about robot-object
constraints and uses them to effectively prune the search space. The new
monotone solver is integrated with a global planner to solve non-monotone
instances with high-quality solutions fast. Furthermore, this work contributes
an effective pre-processing tool to significantly speed up online motion
planning queries for rearrangement in confined spaces. Experiments further
demonstrate that the proposed monotone solver, equipped with the pre-processing
tool, results in 57.3% faster computation and 3 times higher success rate than
state-of-the-art methods. Similarly, the resulting global planner is
computationally more efficient and has a higher success rate, while producing
high-quality solutions for non-monotone instances (i.e., only 1.3 additional
actions are needed on average). Videos of demonstrating solutions on a real
robotic system and codes can be found at
https://github.com/Rui1223/uniform_object_rearrangement.

Robotics;Artificial Intelligence