UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands

Understand

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.