  Robot manipulation in cluttered environments often requires complex and
sequential rearrangement of multiple objects in order to achieve the desired
reconfiguration of the target objects. Due to the sophisticated physical
interactions involved in such scenarios, rearrangement-based manipulation is
still limited to a small range of tasks and is especially vulnerable to
physical uncertainties and perception noise. This paper presents a planning
framework that leverages the efficiency of sampling-based planning approaches,
and closes the manipulation loop by dynamically controlling the planning
horizon. Our approach interleaves planning and execution to progressively
approach the manipulation goal while correcting any errors or path deviations
along the process. Meanwhile, our framework allows the definition of
manipulation goals without requiring explicit goal configurations, enabling the
robot to flexibly interact with all objects to facilitate the manipulation of
the target ones. With extensive experiments both in simulation and on a real
robot, we evaluate our framework on three manipulation tasks in cluttered
environments: grasping, relocating, and sorting. In comparison with two
baseline approaches, we show that our framework can significantly improve
planning efficiency, robustness against physical uncertainties, and task
success rate under limited time budgets.
