  In our daily life, cluttered objects are everywhere, from scattered
stationery and books cluttering the table to bowls and plates filling the
kitchen sink. Retrieving a target object from clutters is an essential while
challenging skill for robots, for the difficulty of safely manipulating an
object without disturbing others, which requires the robot to plan a
manipulation sequence and first move away a few other objects supported by the
target object step by step. However, due to the diversity of object
configurations (e.g., categories, geometries, locations and poses) and their
combinations in clutters, it is difficult for a robot to accurately infer the
support relations between objects faraway with various objects in between. In
this paper, we study retrieving objects in complicated clutters via a novel
method of recursively broadcasting the accurate local dynamics to build a
support relation graph of the whole scene, which largely reduces the complexity
of the support relation inference and improves the accuracy. Experiments in
both simulation and the real world demonstrate the efficiency and effectiveness
of our method.
