Task-parameterized movement representation, as an approach for the generalization of demonstrations, is used to represent data from multiple local perspectives within the global reference frame, through which more accurate information about multiple aspects of the movement is given. The estimated transformation between the different perspectives and the global reference frame in task parameter inference can be used for gesture recognition.
In this thesis, task parameter inference in the application of human-robot interaction, a method called TP-inference approach, is investigated. It consists of a combination of task parameter inference and task parameter movement retrieval. A task-driven model is used to generalize the demonstration data and the task parameter inference is achieved by using the orthogonal Procrustes analysis. The TP-inference approach is tested for various static tasks and is compared to the Probabilistic Movement Primitive (ProMP) approach [1]. The test results indicate that for simple and or similar movement of the human and robot, the TP-inference approach performs less accurate than the ProMP. For complex movements the TP-inference preforms more accurate than the ProMP.

[1] M. Ewerton, G. Neumann, R. Lioutikov, H. B. Amor, J. Peters, and G. Maeda, Learning multiple collaborative tasks with a mixture of interaction primitives, in 2015 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2015) pp. 1535–1542