Constraining the approach direction of grasps is important when picking
objects in confined spaces, such as when emptying a shelf. Yet, such
capabilities are not available in state-of-the-art data-driven grasp sampling
methods that sample grasps all around the object. In this work, we address the
specific problem of training approach-constrained data-driven grasp samplers
and how to generate good grasping directions automatically. Our solution is
GoNet: a generative grasp sampler that can constrain the grasp approach
direction to lie close to a specified direction. This is achieved by
discretizing SO(3) into bins and training GoNet to generate grasps from 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.Comment: IROS 2023 submissio