Rigid and Non-Rigid Classification Using Interactive Perception

Robotics research tends to focus upon either non-contact sensing or machine manipulation, but not both. This paper explores the benefits of combining the two by addressing the problem of classifying unknown objects, such as found in service robot applications. In the proposed approach, an object lies on a flat background, and the goal of the robot is to interact with and classify each object so that it can be studied further. The algorithm considers each object to be classified using color, shape, and flexibility. Experiments on a number of different objects demonstrate the ability of efficiently classifying and labeling each item through interaction.