Active learning using mean shift optimization for robot grasping

— When children learn to grasp a new object, they often know several possible grasping points from observing a parent’s demonstration and subsequently learn better grasps by trial and error. From a machine learning point of view, this process is an active learning approach. In this paper, we present a new robot learning framework for reproducing this ability in robot grasping. For doing so, we chose a straightforward approach: first, the robot observes a few good grasps by demonstration and learns a value function for these grasps using Gaussian process regression. Subsequently, it chooses grasps which are optimal with respect to this value function using a mean-shift optimization approach, and tries them out on the real system. Upon every completed trial, the value function is updated, and in the following trials it is more likely to choose even better grasping points. This method exhibits fast learning due to the data-efficiency of the Gaussian process regression framework and ...

• Type: Conference
• Year: 2009
• Where: IROS
• Authors: Oliver Kroemer, Renaud Detry, Justus H. Piater, Jan Peters
• Keywords: Gaussian Process Regression, Grasping Points, IROS 2009, Robotics, Value Function