Imputation of Missing Values Affecting the Software Performance of Component-based Robots

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 a ect 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.