Learning stable pushing locations

Abstract

We present a method by which a robot learns to predict effective push-locations as a function of object shape. The robot performs push experiments at many contact locations on multiple objects and records local and global shape features at each point of contact. The robot observes the outcome trajectories of the manipulations and computes a novel push-stability score for each trial. The robot then learns a regression function in order to predict push effectiveness as a function of object shape. This mapping allows the robot to select effective push locations for subsequent objects whether they are previously manipulated instances, new instances from previously encountered object classes, or entirely novel objects. In the totally novel object case, the local shape property coupled with the overall distribution of the object allows for the discovery of effective push locations. These results are demonstrated on a mobile manipulator robot pushing a variety of household objects on a tabletop surface.