Interactive learning of visually symmetric objects

Abstract

This paper describes a robotic system that learns visual models of symmetric objects autonomously. Our robot learns by physically interacting with an object using its end effector. This departs from eye-in-hand systems that move the camera while keeping the scene static. Our robot leverages a simple nudge action to obtain the motion segmentation of an object in stereo. The robot uses the segmentation results to pick up the object. The robot collects training images by rotating the grasped object in front of a camera. Robotic experiments show that this interactive object learning approach can deal with top-heavy and fragile objects. Trials confirm that the robot-learned object models allow robust object recognition.