  This work addresses the problem of learning approach-constrained data-driven
grasp samplers. To this end, we propose GoNet: a generative grasp sampler that
can constrain the grasp approach direction to a subset of SO(3). The key
insight is to discretize SO(3) into a predefined number of bins and train GoNet
to generate grasps whose approach directions are within those bins. At
run-time, the bin aligning with the second largest principal component of the
observed point cloud is selected. GoNet is benchmarked against GraspNet, a
state-of-the-art unconstrained grasp sampler, in an unconfined grasping
experiment in simulation and on an unconfined and confined grasping experiment
in the real world. The results demonstrate that GoNet achieves higher
success-over-coverage in simulation and a 12%-18% higher success rate in
real-world table-picking and shelf-picking tasks than the baseline.
