Verbal Programming of Robot Behavior
This addresses the need for personalized robot adaptation in home environments, though it appears incremental as it builds on existing verbal programming approaches.
The paper tackles the problem of customizing home robot behavior through one-shot learning by allowing users to verbally explain tasks, and it demonstrates the ALIA cognitive architecture effectively incorporating such advice on a small robot.
Home robots may come with many sophisticated built-in abilities, however there will always be a degree of customization needed for each user and environment. Ideally this should be accomplished through one-shot learning, as collecting the large number of examples needed for statistical inference is tedious. A particularly appealing approach is to simply explain to the robot, via speech, what it should be doing. In this paper we describe the ALIA cognitive architecture that is able to effectively incorporate user-supplied advice and prohibitions in this manner. The functioning of the implemented system on a small robot is illustrated by an associated video.