Ranking the good points: A comprehensive method for humanoid robots to grasp unknown objects

We propose a grasping pipeline to deal with unknown objects in the real world. We focus on power grasp, which is characterized by large areas of contact between the object and the surfaces of the palm and fingers. Our method seeks object regions that match the curvature of the robot´s palm. The entire procedure relies on binocular vision, which provides a 3D point cloud of the visible part of the object. The obtained point cloud is segmented in smooth surfaces. A score function measures the quality of the graspable points on the basis of the surface they belong to. A component of the score function is learned from experience and it is used to map the curvature of the object surfaces to the curvature of the robot´s hand.The user can further provide top-down information on the preferred grasping regions. We guarantee the feasibility of a chosen hand configuration by measuring its manipulability. We prove the effectiveness of the proposed approach by tasking a humanoid robot to grasp a number of unknown real objects.