  Transparent objects are prevalent across many environments of interest for
dexterous robotic manipulation. Such transparent material leads to considerable
uncertainty for robot perception and manipulation, and remains an open
challenge for robotics. This problem is exacerbated when multiple transparent
objects cluster into piles of clutter. In household environments, for example,
it is common to encounter piles of glassware in kitchens, dining rooms, and
reception areas, which are essentially invisible to modern robots. We present
the GlassLoc algorithm for grasp pose detection of transparent objects in
transparent clutter using plenoptic sensing. GlassLoc classifies graspable
locations in space informed by a Depth Likelihood Volume (DLV) descriptor. We
extend the DLV to infer the occupancy of transparent objects over a given space
from multiple plenoptic viewpoints. We demonstrate and evaluate the GlassLoc
algorithm on a Michigan Progress Fetch mounted with a first-generation Lytro.
The effectiveness of our algorithm is evaluated through experiments for grasp
detection and execution with a variety of transparent glassware in minor
clutter.
