Examining the impact of data augmentation for psychomotor skills training in human-robot interaction

Training psychomotor skills for human-robot interaction is generally done with a human trainer educating the human on how to handle the robot effectively and interact with it safely and efficiently. The dynamic interaction between a robot and a human requires complex machine learning algorithms to be modeled, and these algorithms rely on a large amount of data to be trained. Such data are collected by sensors when a human interacts with a robot. Consequently, the data must be annotated by an expert. Finally, with the annotated data, a psychomotor skills training model can be created to assist the training process. This is a time intensive and costly process. To ease the costs and cut down collection time, we propose the use of data augmentation.