  Viewpoint planning is an important task in any application where objects or
scenes need to be viewed from different angles to achieve sufficient coverage.
The mapping of confined spaces such as shelves is an especially challenging
task since objects occlude each other and the scene can only be observed from
the front, posing limitations on the possible viewpoints. In this paper, we
propose a deep reinforcement learning framework that generates promising views
aiming at reducing the map entropy. Additionally, the pipeline extends standard
viewpoint planning by predicting adequate minimally invasive push actions to
uncover occluded objects and increase the visible space. Using a 2.5D occupancy
height map as state representation that can be efficiently updated, our system
decides whether to plan a new viewpoint or perform a push. To learn feasible
pushes, we use a neural network to sample push candidates on the map based on
training data provided by human experts. As simulated and real-world
experimental results with a robotic arm show, our system is able to
significantly increase the mapped space compared to different baselines, while
the executed push actions highly benefit the viewpoint planner with only minor
changes to the object configuration.
