Effective Task Training Strategies for Instructional Robots
Effective Task Training Strategies for Instructional Robots
复制标题
教学机器人的有效任务训练策略
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Bilge Mutlu
中科院分区：
文献类型：
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作者：
Allison Sauppé;Bilge Mutlu
从实验室教学到装配培训，机器人预计将扮演的一个关键角色是指导用户完成物理任务。虽然任务教学需要广泛的能力，如有效地使用口头和非口头语言，但对教学机器人的基本要求是以最大限度地提高学生对任务的理解和表现的方式为其提供任务指令。在本文中，我们提出了一个自主教学机器人系统，并探讨不同的教学策略如何影响用户的性能和体验。我们收集了在管道装配任务中人类装配工与受训者相互作用的数据。我们的分析确定了两个关键的教学策略：（1）将指令分组在一起，（2）总结后续指令的结果。我们将这些策略应用到一个类似人类的机器人中，该机器人可以自主地指导用户完成相同的管道装配任务。为了实现自主教学，我们还开发了一种修复机制，使机器人能够纠正错误和误解。在人机交互研究的教学策略的评估表明，采用分组策略导致更快的任务完成，并增加与机器人的融洽关系，虽然它也增加了任务故障的数量。我们的教学策略模型和研究结果为教学机器人的设计提供了强有力的启示。
From teaching in labs to training for assembly, a key role that robots are expected to play is to instruct their users in completing physical tasks. While task instruction requires a wide range of capabilities, such as effective use of verbal and nonverbal language, a fundamental requirement for an instructional robot is to provide its students with task instructions in a way that maximizes their understanding of and performance in the task. In this paper, we present an autonomous instructional robot system and investigate how different instructional strategies affect user performance and experience. We collected data on human instructor-trainee interactions in a pipe-assembly task. Our analysis identified two key instructional strategies: (1) grouping instructions together and (2) summarizing the outcome of subsequent instructions. We implemented these strategies into a humanlike robot that autonomously instructed its users in the same pipe-assembly task. To achieve autonomous instruction, we also developed a repair mechanism that enabled the robot to correct mistakes and misunderstandings. An evaluation of the instructional strategies in a human-robot interaction study showed that employing the grouping strategy resulted in faster task completion and increased rapport with the robot, although it also increased the number of task breakdowns. Our model of instructional strategies and study findings offer strong implications for the design of instructional robots.