论文标题
非马尔可夫任务的互动机器人培训
Interactive Robot Training for Non-Markov Tasks
论文作者
论文摘要
使用形式语言为机器人定义声音和完整的规格很具有挑战性，而直接从演示中学习形式规格可以导致过度受限的任务策略。在本文中，我们提出了一个贝叶斯互动机器人培训框架，该框架使机器人可以从教师提供的两个演示中学习，以及对机器人任务执行的评估。我们还提出了一种积极的学习方法 - 受不确定性抽样的启发 - 以最可接受程度确定任务执行。通过模拟实验，我们证明了我们的主动学习方法与纯粹从示范中学习的方法相比，以等效或更大的相似性来识别教师的预期任务规范。最后，我们通过教授机器人设置餐桌的用户研究来证明方法在现实世界中的功效。
Defining sound and complete specifications for robots using formal languages is challenging, while learning formal specifications directly from demonstrations can lead to over-constrained task policies. In this paper, we propose a Bayesian interactive robot training framework that allows the robot to learn from both demonstrations provided by a teacher, and that teacher's assessments of the robot's task executions. We also present an active learning approach -- inspired by uncertainty sampling -- to identify the task execution with the most uncertain degree of acceptability. Through a simulated experiment, we demonstrate that our active learning approach identifies a teacher's intended task specification with an equivalent or greater similarity when compared to an approach that learns purely from demonstrations. Finally, we demonstrate the efficacy of our approach in a real-world setting through a user-study based on teaching a robot to set a dinner table.
