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

6-DoF Grasp Planning using Fast 3D Reconstruction and Grasp Quality CNN