  Motivated by the stringent requirements of unstructured real-world where a
plethora of unknown objects reside in arbitrary locations of the surface, we
propose a voxel-based deep 3D Convolutional Neural Network (3D CNN) that
generates feasible 6-DoF grasp poses in unrestricted workspace with
reachability awareness. Unlike the majority of works that predict if a proposed
grasp pose within the restricted workspace will be successful solely based on
grasp pose stability, our approach further learns a reachability predictor that
evaluates if the grasp pose is reachable or not from robot's own experience. To
avoid the laborious real training data collection, we exploit the power of
simulation to train our networks on a large-scale synthetic dataset. This work
is an early attempt that simultaneously evaluates grasping reachability from
learned knowledge while proposing feasible grasp poses with 3D CNN.
Experimental results in both simulation and real-world demonstrate that our
approach outperforms several other methods and achieves 82.5% grasping success
rate on unknown objects.
