An integrated object representation for recognition and grasping

As a step towards systems that can acquire knowledge automatically we have designed a system that can learn new objects with a minimum of user interaction and implemented it on our robot platform GripSee [1]. A novel object is placed into the robot’s gripper in order to define a default orientation and a default grip. The robot then places the object on a turning table and builds up a visual representation that consists of a collection of graphs, labeled with multiscale edges. A user interface that can correct errors in the representation is also part of the system. The visual representation is complemented by a grip library, which contains possible ways of grasping and manipulating the object in a robust manner. We regard this procedure as an example of Human Assisted Learning.