Deep learning of mobile service robots

Abstract

In the last two decades, the development of state -of-the-art artificial intelligence (AI) models has significantly increased the utilization of commercial and task-specific robots in the service domain. The additional level of intelligence introduced by AI models has enabled service robots to coexist within different human environments and collaborate with end-users. One of the most promising AI techniques, Deep Learning (DL), can provide service robots with a wide range of abilities, such as detecting human pose and emotions, understanding natural languages, as well as scene understanding. Achieved abilities can enable mobile service robots to execute specific tasks in real and stochastic environments. Having that in mind, in this chapter, we provide an in-depth analysis of the tasks that are best-suited for DL within the service robots domain. Moreover, the study of the state-of-the-art DL models for object detection, semantic segmentation, and human pose estimation is carried out. In the end, the authors presented a thorough examination of the training process and analysis of the results for one of the most promising convolutional neural network models (DeepLabv3+) used for semantic segmentation.