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MANIPULATION

Quantifying teaching behavior in robot learning from demonstration

Aran Sena, Matthew Howard

发表年份
2019
引用次数
47
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摘要

Learning from demonstration allows for rapid deployment of robot manipulators to a great many tasks, by relying on a person showing the robot what to do rather than programming it. While this approach provides many opportunities, measuring, evaluating, and improving the person’s teaching ability has remained largely unexplored in robot manipulation research. To this end, a model for learning from demonstration is presented here that incorporates the teacher’s understanding of, and influence on, the learner. The proposed model is used to clarify the teacher’s objectives during learning from demonstration, providing new views on how teaching failures and efficiency can be defined. The benefit of this approach is shown in two experiments ([Formula: see text] and [Formula: see text], respectively), which highlight the difficulty teachers have in providing effective demonstrations, and show how [Formula: see text]–180% improvement in teaching efficiency can be achieved through evaluation and feedback shaped by the proposed framework, relative to unguided teaching.

关键词

RobotComputer scienceSoftware deploymentHuman–computer interactionArtificial intelligenceSoftware engineering

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