ロボットの見まね学習における教示行動の定量化
Quantifying Teaching Behaviour in Robot Learning from Demonstration
見まね学習において教師の学習者理解と影響を組み込んだモデルを提案し、教示の失敗や効率を定義する新たな視点を与え、評価とフィードバックにより教示効率が約169〜180%改善することを2つの実験で示した。
著者: Aran Sena, Matthew J Howard
分類: cs.RO, cs.AI, cs.HC, cs.LG
原文アブストラクト
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.
