Abstract Intelligent robots are foreseen as a technology that would be soon present in most public and private environments. In order to increase the trust of humans, robotic systems must be reliable while both response and down times are minimized. In keeping with this idea, present paper proposes the application of machine learning (regression models more precisely) to preprocess data in order to improve the detection of failures. Such failures deeply affect the performance of the software components embedded in human-interacting robots. To address one of the most common problems of real-life datasets (missing values), some traditional (such as linear regression) as well as innovative (decision tree and neural network) models are applied. The aim is to impute missing values with minimum error in order to improve the quality of data and consequently maximize the failure-detection rate. Experiments are run on a public and up-to-date dataset and the obtained results support the viability of the proposed models.

中文翻译：

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影响基于组件的机器人软件性能的缺失值的估算

摘要 智能机器人预计将很快出现在大多数公共和私人环境中。为了增加人类的信任度，机器人系统必须可靠，同时最大限度地减少响应和停机时间。为了与这个想法保持一致，本文提出了应用机器学习（更精确的回归模型）来预处理数据，以改进故障检测。此类故障会严重影响人机交互机器人中嵌入的软件组件的性能。为了解决现实生活中数据集最常见的问题之一（缺失值），应用了一些传统（例如线性回归）和创新（决策树和神经网络）模型。目的是以最小的误差来估算缺失值，以提高数据质量，从而最大限度地提高故障检测率。实验在公共和最新数据集上运行，获得的结果支持所提出模型的可行性。