Active Explicable Planning for Human-Robot Teaming
Active Explicable Planning for Human-Robot Teaming
复制标题
人机协作的主动可解释规划
DOI：
10.1145/3434074.3447154
复制
发表时间：
2021
期刊：
影响因子：
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通讯作者：
Zhang, Yu
中科院分区：
文献类型：
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作者：
Hanni, Akkamahadevi;Zhang, Yu
智能机器人正在重新定义自主任务，但距离完全有能力协助人类完成日常任务还很远。协作的一个重要要求是清楚地了解彼此的期望和能力。缺少这些可能会导致严重的问题，如团队成员之间的松散协调，团队绩效低下，最终导致任务失败。因此，重要的是机器人的行为可解释，使自己的理解人类。这里的挑战之一是，人类的期望往往是隐藏的，并且随着人类与机器人的互动而动态变化。现有的计划可解释性方法通常假设人类的期望是已知的和静态的。在本文中，我们提出了主动可解释规划的思想来解决这一问题。我们应用贝叶斯方法来建模和预测动态的人类信念，使其更具预见性，因此可以在不影响可解释性的情况下生成更有效的计划。我们假设，与现有方法生成的计划相比，主动的可解释计划可以更有效，同时也更可解释。从Mturk研究的初步结果中，我们发现我们的方法有效地捕获了人类的动态信念，可以用来产生有效的和可解释的行为，这些行为受益于动态变化的期望。
Intelligent robots are redefining autonomous tasks but are still far from being fully capable of assisting humans in day to day tasks. An important requirement of collaboration is to have a clear understanding of each other's expectations and capabilities. Lack of which may lead to serious issues such as loose coordination between teammates, ineffective team performance, and ultimately mission failures. Hence, it is important for the robot to behave explicably to make themselves understandable to the human. One of the challenges here is that the expectations of the human are often hidden and dynamically changing as the human interacts with the robot. Existing approaches in plan explicability often assume the human's expectations are known and static. In this paper, we propose the idea of active explicable planning to address this issue. We apply a Bayesian approach to model and predict dynamic human beliefs to be more anticipatory, and hence can generate more efficient plans without impacting explicability. We hypothesize that active explicable plans can be more efficient and more explicable at the same time, compared to the plans generated by existing methods. From the preliminary results of Mturk study, we find that our approach effectively captures the dynamic belief of the human which can be used to generate efficient and explicable behavior that benefits from dynamically changing expectations.
DOI：
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发表时间：
2018-07
期刊：
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影响因子：
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作者：
Aleck M. MacNally;N. Lipovetzky;Miquel Ramírez;A. Pearce
通讯作者：
Aleck M. MacNally;N. Lipovetzky;Miquel Ramírez;A. Pearce
DOI：
10.1007/978-3-642-01465-9
发表时间：
2008
期刊：
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影响因子：
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作者：
David Hutchison;T. Kanade;J. Kittler;M.Kleinberg Jon
通讯作者：
David Hutchison;T. Kanade;J. Kittler;M.Kleinberg Jon