A Variational Time Series Feature Extractor for Action Prediction

The problem of recognizing actions or activities has been widely addressed in the computer vision research community: it consists in the classification of a fully or partially observed action, typically observed through cameras or external motion capture [2]. In robotics, recognizing the human activity is paramount for enabling a proper interaction and providing assistance to the human: an assistive device or prosthetics could switch control modes depending on the current human activity (e.g., walking or sitting) [3], [4]; a mobile robot may adapt its navigation depending on the prediction of the human motion [5]. More generally, prediction is important to provide the robot with anticipation capabilities [6]. In collaborative robotics applications in manufacturing, such as in assembly lines, recognizing the current activity of the operator is necessary for ergonomics evaluations [7] and for the optimization of the robot actions. However, there are two critical issues that prevent the direct application of existing techniques in such scenarios. The first issue is the availability of external sensing devices (cameras or motion captures) that poses constraints on the application for many tasks and application scenarios.