Compressing Grasping Experience into a Dictionary of Prototypical Grasp-predicting Parts

Abstract

We present a real-world robotic agent that is capable of transferring grasping strategies across objects that share similar parts. The agent transfers grasps across objects by identifying, from examples provided by a teacher, parts by which objects are often grasped in a similar fashion. It then uses these parts to identify grasping points onto novel objects. Because most human environments make it infeasible to pre-program grasping behaviors for every object the robot might encounter, grasping novel objects is a key issue in human-friendly robotics. Recent approaches to grasping novel objects aim at devising a direct mapping from visual features to grasp parameters. A central question in such approaches is what visual features to use. Some authors have shown that grasps can be computed from local visual features