Teaching a Robot Where Doors and Drawers Are and How To Handle Them

Abstract

We address the problem of teaching a service robot to detect doors and drawers in indoor environments. We propose a robust and accurate method in which a human demonstrates to the robot how to open doors and drawers that the robot is expected to operate in its future use. The proposed algorithm creates a model of a door or drawer from a sequence of RGB-D images and inserts it into an environment map. The model contains information about the size of the door panel or drawer front, as well as the position and orientation of the joint axis. This augmented environment map is then used by the robot to detect the target object in its environment and estimate its state.