“做这个”——对纠正指令做出充分反应的机器人

IF 4.2 Q2 ROBOTICS
Christopher Thierauf, Ravenna Thielstrom, Bradley Oosterveld, Will Becker, Matthias Scheutz
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引用次数: 0

摘要

自然语言指令在给自主机器人分配任务和快速教授新知识方面是有效的。然而,人类教师并不完美,有时可能会犯错误,当他们注意到自己的指示中的错误时,他们会纠正自己。在本文中,我们引入了一个完整的机器人行为系统,在任务指令和动作执行过程中处理这种纠正。然后,我们通过口语在两个任务中演示其在集成认知机器人架构中的操作:导航和检索任务以及饭菜组装任务。口头纠正发生在口头教导任务序列之前、期间和之后,这表明所提出的方法不仅能够快速纠正指令生成的语义,而且能够以与人类行为和期望相比合理的方式纠正明显的机器人行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
“Do this instead” – Robots that Adequately Respond to Corrected Instructions
Natural language instructions are effective at tasking autonomous robots and for teaching them new knowledge quickly. Yet, human instructors are not perfect and are likely to make mistakes at times, and will correct themselves when they notice errors in their own instructions. In this paper, we introduce a complete system for robot behaviors to handle such corrections, during both task instruction and action execution. We then demonstrate its operation in an integrated cognitive robotic architecture through spoken language in two tasks: a navigation and retrieval task and a meal assembly task. Verbal corrections occur before, during, and after verbally taught sequences of tasks, demonstrating that the proposed methods enable fast corrections not only of the semantics generated from the instructions, but also of overt robot behavior in a manner shown to be reasonable when compared to human behavior and expectations.
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来源期刊
ACM Transactions on Human-Robot Interaction
ACM Transactions on Human-Robot Interaction Computer Science-Artificial Intelligence
CiteScore
7.70
自引率
5.90%
发文量
65
期刊介绍: ACM Transactions on Human-Robot Interaction (THRI) is a prestigious Gold Open Access journal that aspires to lead the field of human-robot interaction as a top-tier, peer-reviewed, interdisciplinary publication. The journal prioritizes articles that significantly contribute to the current state of the art, enhance overall knowledge, have a broad appeal, and are accessible to a diverse audience. Submissions are expected to meet a high scholarly standard, and authors are encouraged to ensure their research is well-presented, advancing the understanding of human-robot interaction, adding cutting-edge or general insights to the field, or challenging current perspectives in this research domain. THRI warmly invites well-crafted paper submissions from a variety of disciplines, encompassing robotics, computer science, engineering, design, and the behavioral and social sciences. The scholarly articles published in THRI may cover a range of topics such as the nature of human interactions with robots and robotic technologies, methods to enhance or enable novel forms of interaction, and the societal or organizational impacts of these interactions. The editorial team is also keen on receiving proposals for special issues that focus on specific technical challenges or that apply human-robot interaction research to further areas like social computing, consumer behavior, health, and education.
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