  Pushing objects through cluttered scenes is a challenging task, especially
when the objects to be pushed have initially unknown dynamics and touching
other entities has to be avoided to reduce the risk of damage. In this paper,
we approach this problem by applying deep reinforcement learning to generate
pushing actions for a robotic manipulator acting on a planar surface where
objects have to be pushed to goal locations while avoiding other items in the
same workspace. With the latent space learned from a depth image of the scene
and other observations of the environment, such as contact information between
the end effector and the object as well as distance to the goal, our framework
is able to learn contact-rich pushing actions that avoid collisions with other
objects. As the experimental results with a six degrees of freedom robotic arm
show, our system is able to successfully push objects from start to end
positions while avoiding nearby objects. Furthermore, we evaluate our learned
policy in comparison to a state-of-the-art pushing controller for mobile robots
and show that our agent performs better in terms of success rate, collisions
with other objects, and continuous object contact in various scenarios.
