解析人类教师对自然交互任务学习的意图
Unpacking Human Teachers' Intentions For Natural Interactive Task Learning
摘要：
交互式任务学习（ITL）是一个新兴的研究议程，研究设计复杂的智能机器人，可以通过自然的人类教师-机器人学习者互动获得新的知识。ITL方法对于设计智能机器人特别有用，这些机器人的行为可以通过与它们合作的人类来调整。各种研究团体都在为ITL提供方法，这项研究的一个很大的子集是\emph{robot-centered}，重点是开发可以快速在线学习的算法。本文从\emph{human-centered}的角度研究ITL问题，为机器人设计提供指导，使人类教师可以自然地教授ITL机器人。在本文中，我们提出了1）互动教学研究（N=10）的定性双向分析，通过它，我们描述了人类教师在教机器人时所打算和执行的动作的各个方面; 2）深入讨论了两个参与者所采用的教学方法，以了解个人适应个人教学风格的必要性;和3）ITL机器人设计的要求，基于我们的分析和通知的计算理论的协作互动，SharedPlans。
英文摘要：
Interactive Task Learning (ITL) is an emerging research agenda that studies the design of complex intelligent robots that can acquire new knowledge through natural human teacher-robot learner interactions. ITL methods are particularly useful for designing intelligent robots whose behavior can be adapted by humans collaborating with them. Various research communities are contributing methods for ITL and a large subset of this research is \emph{robot-centered} with a focus on developing algorithms that can learn online, quickly. This paper studies the ITL problem from a \emph{human-centered} perspective to provide guidance for robot design so that human teachers can naturally teach ITL robots. In this paper, we present 1) a qualitative bidirectional analysis of an interactive teaching study (N=10) through which we characterize various aspects of actions intended and executed by human teachers when teaching a robot; 2) an in-depth discussion of the teaching approach employed by two participants to understand the need for personal adaptation to individual teaching styles; and 3) requirements for ITL robot design based on our analyses and informed by a computational theory of collaborative interactions, SharedPlans.
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。
