教示に基づく説明可能AIによる模倣学習手法
Demonstration Based Explainable AI for Learning from Demonstration Methods
逆強化学習に適応的な説明フィードバックを組み込み、成功・失敗軌道を選択的に提示することで、ロボットの性能と教示効率、ユーザ理解を向上させる手法を提案した。
著者: Morris Gu, Elizabeth Croft, Dana Kulic
分類: cs.RO
原文アブストラクト
Learning from Demonstration (LfD) is a powerful type of machine learning that can allow novices to teach and program robots to complete various tasks. However, the learning process for these systems may still be difficult for novices to interpret and understand, making effective teaching challenging. Explainable artificial intelligence (XAI) aims to address this challenge by explaining a system to the user. In this work, we investigate XAI within LfD by implementing an adaptive explanatory feedback system on an inverse reinforcement learning (IRL) algorithm. The feedback is implemented by demonstrating selected learnt trajectories to users. The system adapts to user teaching by categorizing and then selectively sampling trajectories shown to a user, to show a representative sample of both successful and unsuccessful trajectories. The system was evaluated through a user study with 26 participants teaching a robot a navigation task. The results of the user study demonstrated that the proposed explanatory feedback system can improve robot performance, teaching efficiency and user understanding of the robot.
