  Non-prehensile planar pushing is a challenging task due to its underactuated
nature with hybrid-dynamics, where a robot needs to reason about an object's
long-term behaviour and contact-switching, while being robust to contact
uncertainty. The presence of clutter in the environment further complicates
this task, introducing the need to include more sophisticated spatial analysis
to avoid collisions. Building upon prior work on reinforcement learning (RL)
with multimodal categorical exploration for planar pushing, in this paper we
incorporate location-based attention to enable robust navigation through
clutter. Unlike previous RL literature addressing this obstacle avoidance
pushing task, our framework requires no predefined global paths and considers
the target orientation of the manipulated object. Our results demonstrate that
the learned policies successfully navigate through a wide range of complex
obstacle configurations, including dynamic obstacles, with smooth motions,
achieving the desired target object pose. We also validate the transferability
of the learned policies to robotic hardware using the KUKA iiwa robot arm.
