  Recent consumer demand for home robots has accelerated performance of robotic
grasping. However, a key component of the perception pipeline, the depth
camera, is still expensive and inaccessible to most consumers. In addition,
grasp planning has significantly improved recently, by leveraging large
datasets and cloud robotics, and by limiting the state and action space to
top-down grasps with 4 degrees of freedom (DoF). By leveraging multi-view
geometry of the object using inexpensive equipment such as off-the-shelf RGB
cameras and state-of-the-art algorithms such as Learn Stereo Machine
(LSM\cite{kar2017learning}), the robot is able to generate more robust grasps
from different angles with 6-DoF. In this paper, we present a modification of
LSM to graspable objects, evaluate the grasps, and develop a 6-DoF grasp
planner based on Grasp-Quality CNN (GQ-CNN\cite{mahler2017dex}) that exploits
multiple camera views to plan a robust grasp, even in the absence of a possible
top-down grasp.
