  We propose an approach to multi-modal grasp detection that jointly predicts
the probabilities that several types of grasps succeed at a given grasp pose.
Given a partial point cloud of a scene, the algorithm proposes a set of
feasible grasp candidates, then estimates the probabilities that a grasp of
each type would succeed at each candidate pose. Predicting grasp success
probabilities directly from point clouds makes our approach agnostic to the
number and placement of depth sensors at execution time. We evaluate our system
both in simulation and on a real robot with a Robotiq 3-Finger Adaptive Gripper
and compare our network against several baselines that perform fewer types of
grasps. Our experiments show that a system that explicitly models grasp type
achieves an object retrieval rate 8.5% higher in a complex cluttered
environment than our highest-performing baseline.
