A 3D Vision based Object Grasping Posture Learning System for Home Service Robots

This paper proposes a 3D vision based object grasping posture learning system. In this system, the robot recognizes the orientation of the object to decide the grasping posture, whereas selects a feasible grasping point by detecting the surrounding. When the planned posture is not good enough, the proposed learning system adjusts the position of the end effector real time. The learning system is inspired by a book entitled, Thinking, Fast and Slow, and consists of two subsystems. The subsystem I judges whether the pose of the object is learned before, and plans a grasping posture by past experience. When the pose of the object is not learned before, the subsystem II learns a position adjustment by the real time information form the motor angels and the images. Finally, the method proposed in this paper is applied to the home service robot and is proven the feasibility by the experimental results.