Incremental action recognition and generalizing motion generation based on goal-directed features

The ability to recognize human actions is a fundamental problem in many areas of robotics research concerned with human-robot interaction or learning from human demonstration. In this paper, we present a new integrated approach to identifying and recognizing actions in human movement sequences and their reproduction in unknown situations. We propose a set of task-space features to construct probabilistic models of action classes. Based on this representation, we suggest a combined segmentation and classification algorithm which processes data non-greedily using an incremental lookahead to reliably locate transitions between actions. In a programming by demonstration scenario, our action models afford the generalization and reproduction of learned movements to previously unseen situations. To evaluate the performance of our approach, we consider typical manipulation tasks in a table top setting. In a sequence of human demonstrations, our approach successfully extracts and recognizes actions from different classes and subsequently generalizes them to unknown situations.