  This work explores the use of topological tools for achieving effective
non-prehensile manipulation in cluttered, constrained workspaces. In
particular, it proposes the use of persistent homology as a guiding principle
in identifying the appropriate non-prehensile actions, such as pushing, to
clean a cluttered space with a robotic arm so as to allow the retrieval of a
target object. Persistent homology enables the automatic identification of
connected components of blocking objects in the space without the need for
manual input or tuning of parameters. The proposed algorithm uses this
information to push groups of cylindrical objects together and aims to minimize
the number of pushing actions needed to reach to the target. Simulated
experiments in a physics engine using a model of the Baxter robot show that the
proposed topology-driven solution is achieving significantly higher success
rate in solving such constrained problems relatively to state-of-the-art
alternatives from the literature. It manages to keep the number of pushing
actions low, is computationally efficient and the resulting decisions and
motion appear natural for effectively solving such tasks.
