Robot Grasp Learning by Demonstration without Predefined Rules

Abstract

A learning-based approach to autonomous robot grasping is presented. Pattern recognition techniques are used to measure the similarity between a set of previously stored example grasps and all the possible candidate grasps for a new object. Two sets of features are defined in order to characterize grasps: point attributes describe the surroundings of a contact point; point-set attributes describe the relationship between the set of n contact points (assuming an n-fingered robot gripper is used). In the experiments performed, the nearest neighbour classifier outperforms other approaches like multilayer perceptrons, radial basis functions or decision trees, in terms of classification accuracy, while computational load is not excessive for a real time application (a grasp is fully synthesized in 0.2 seconds). The results obtained on a synthetic database show that the proposed system is able to imitate the grasping behaviour of the user (e.g. the system learns to grasp a mug by its handle). All the code has been made available for testing purposes.