让机器人通过演示学习时，用可视化轨迹解释决策，提升教学效率。
Demonstration Based Explainable AI for Learning from Demonstration Methods
- 用逆强化学习+自适应轨迹展示，动态反馈学习过程
- 26人实验显示：性能、教学速度与用户理解均显著提升
- 适合希望理解机器人行为的非专业教学者使用
从演示学习（LfD）是一种强大的机器学习方法，使新手能够教会机器人完成各种任务。然而，这些系统的学习过程对新手而言仍难理解，导致有效教学困难。可解释人工智能（XAI）旨在通过向用户提供系统解释来解决这一问题。本文在逆强化学习（IRL）算法中引入自适应解释性反馈机制，通过展示精选学得的轨迹来实现反馈。系统根据用户教学情况，对轨迹进行分类并有选择地采样，以呈现成功与失败轨迹的代表性样本。通过26名参与者参与的导航任务教学实验验证，结果表明该解释性反馈系统能有效提升机器人性能、教学效率以及用户对机器人的理解。
原文摘要 · Abstract (English)
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.
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