  To achieve a successful grasp, gripper attributes such as its geometry and
kinematics play a role as important as the object geometry. The majority of
previous work has focused on developing grasp methods that generalize over
novel object geometry but are specific to a certain robot hand. We propose
UniGrasp, an efficient data-driven grasp synthesis method that considers both
the object geometry and gripper attributes as inputs. UniGrasp is based on a
novel deep neural network architecture that selects sets of contact points from
the input point cloud of the object. The proposed model is trained on a large
dataset to produce contact points that are in force closure and reachable by
the robot hand. By using contact points as output, we can transfer between a
diverse set of multifingered robotic hands. Our model produces over 90% valid
contact points in Top10 predictions in simulation and more than 90% successful
grasps in real world experiments for various known two-fingered and
three-fingered grippers. Our model also achieves 93%, 83% and 90% successful
grasps in real world experiments for an unseen two-fingered gripper and two
unseen multi-fingered anthropomorphic robotic hands.
