Learning Contact Locations for Pushing and Orienting Unknown Objects

Abstract

We present a method by which a robot learns to predict effective contact locations for pushing 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. Each trial attempts to either push the object in a straight line or to rotate the object to a new orientation. The robot observes the outcome trajectories of the manipulations and computes either a push-stability or rotate-push score for each trial. The robot then learns a regression function for each score in order to predict push effectiveness as a function of object shape. With this mapping, the robot can infer effective push locations for subsequent objects from their shapes, regardless of whether they belong to a previously encountered object class. These results are demonstrated on a mobile manipulator robot pushing a variety of household objects on a tabletop surface.