  In this paper, we introduce a method to deal with the problem of robot local
path planning among pushable objects -- an open problem in robotics. In
particular, we achieve that by training multiple agents simultaneously in a
physics-based simulation environment, utilizing an Advantage Actor-Critic
algorithm coupled with a deep neural network. The developed online policy
enables these agents to push obstacles in ways that are not limited to axial
alignments, adapt to unforeseen changes in obstacle dynamics instantaneously,
and effectively tackle local path planning in confined areas. We tested the
method in various simulated environments to prove the adaptation effectiveness
to various unseen scenarios in unfamiliar settings. Moreover, we have
successfully applied this policy on an actual quadruped robot, confirming its
capability to handle the unpredictability and noise associated with real-world
sensors and the inherent uncertainties present in unexplored object pushing
tasks.
