Efficiently finding poses for multiple grasp types with partial point clouds by uncoupling grasp shape and scale

We present an algorithm that discovers grasp pose solutions for multiple grasp types for a multi-fingered mechanical gripper using partially-sensed point clouds of unknown objects. The algorithm introduces two key ideas: (1) a histogram of finger contact normals is used to represent a grasp “shape” to guide a gripper orientation search in a histogram of object(s) surface normals, and (2) voxel grid representations of gripper contacts and object(s) are cross-correlated to match finger contact points, i.e. grasp “scale`”, to discover a grasp pose. Collision constraints are incorporated in the cross-correlation computation. We show via simulations and preliminary experiments that (1) grasp poses for three grasp types (i.e. lateral, power, and tripodal) are found quickly without interrupting the robot’s motion, (2) the quality of grasp pose solutions is consistent with respect to voxel resolution changes for both partial and complete point cloud scans, (3) grasp type definitions are scalable for n-contacts and can incorporate constraints for collision checks in one integrated step, and (4) planned grasp poses are successfully executed with a mechanical gripper demonstrating the robustness of grasp pose solutions.