Towards Industrial Robot Learning from Demonstration

Abstract

Learning from demonstration (LfD) provides an easy and intuitive way to program robot behaviours, potentially reducing development time and costs tremendously. This is especially appealing for manufacturers interested in using industrial manipulators for high-mix production, since this technique enables fast and flexible modifications to the robot behaviours and is thus suitable to teach the robot to perform a wide range of tasks regularly. We define a set of criteria to assess the applicability of state-of-the-art LfD frameworks in the industry. A three-stage LfD method is then proposed, which incorporates human-in-the-loop adaptation to iteratively correct a batch-learned policy to improve accuracy and precision. The system will then transit to open-loop execution of the task to enhance production speed, by removing the human teacher from the feedback loop. The proposed LfD framework addresses all criteria set in this work.