arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1905.04218cs.ROcs.AIcs.HCcs.LG

Quantifying Teaching Behaviour in Robot Learning from Demonstration

King's College London(伦敦国王学院)

Aran Sena, Matthew J Howard

英文摘要:

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 which 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 (N=30 and N=36, respectively), which highlight the difficulty teachers have in providing effective demonstrations, and show how ~169-180% improvement in teaching efficiency can be achieved through evaluation and feedback shaped by the proposed framework, relative to unguided teaching.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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