  In this work, we present several heuristic-based and data-driven active
vision strategies for viewpoint optimization of an arm-mounted depth camera for
the purpose of aiding robotic grasping. These strategies aim to efficiently
collect data to boost the performance of an underlying grasp synthesis
algorithm. We created an open-source benchmarking platform in simulation
(https://github.com/galenbr/2021ActiveVision), and provide an extensive study
for assessing the performance of the proposed methods as well as comparing them
against various baseline strategies. We also provide an experimental study with
a real-world setup by utilizing an existing grasping planning benchmark in the
literature. With these analyses, we were able to quantitatively demonstrate the
versatility of heuristic methods that prioritize certain types of exploration,
and qualitatively show their robustness to both novel objects and the
transition from simulation to the real world. We identified scenarios in which
our methods did not perform well and scenarios that are objectively difficult,
and present a discussion on which avenues for future research show promise.
