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A framework for anomaly detection of robot behaviors

机译:机器人行为异常检测的框架

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Autonomous mobile robots are designed to behave appropriately in changing real-world environments without human intervention. In order to satisfy the requirements of autonomy, the robots have to cope with unknown settings and issues of uncertainties in dynamic and complex environments. A first step is to provide a robot with cognitive capabilities and the ability of self-examination to detect behavioral abnormalities. Unfortunately, most existing anomaly recognition systems are neither suitable for the domain of robotic behavior nor well generalizable. In this work a novel spatial-temporal anomaly detection framework for robotic behaviors is introduced which is characterized by its high level of generalization, the semi-unsupervised manner and its high flexibility in application.
机译:自主移动机器人的设计目的是在不断变化的现实环境中发挥适当作用,而无需人工干预。为了满足自治的要求,机器人必须应对未知的设置以及在动态和复杂环境中的不确定性问题。第一步是为机器人提供认知能力和自我检查能力,以检测行为异常。不幸的是,大多数现有的异常识别系统既不适合机器人行为领域,也不能很好地推广。在这项工作中,介绍了一种新颖的针对机器人行为的时空异常检测框架,该框架的特点是泛化程度高,半无监督方式以及应用中的高度灵活性。

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