Training Humans to Train Robots Dynamic Motor Skills
This addresses the challenge of ensuring effective robot skill acquisition from novice human teachers, though it is incremental as it builds on existing LfD methods.
The paper tackles the problem of improving demonstration quality in learning from demonstration (LfD) by using machine teaching to guide novice teachers, resulting in up to a 66.5% decrease in error for the robot's learnt skill.
Learning from demonstration (LfD) is commonly considered to be a natural and intuitive way to allow novice users to teach motor skills to robots. However, it is important to acknowledge that the effectiveness of LfD is heavily dependent on the quality of teaching, something that may not be assured with novices. It remains an open question as to the most effective way of guiding demonstrators to produce informative demonstrations beyond ad hoc advice for specific teaching tasks. To this end, this paper investigates the use of machine teaching to derive an index for determining the quality of demonstrations and evaluates its use in guiding and training novices to become better teachers. Experiments with a simple learner robot suggest that guidance and training of teachers through the proposed approach can lead to up to 66.5% decrease in error in the learnt skill.