Print Email Facebook Twitter Task Parameter Inference in Human-Robot Interaction Title Task Parameter Inference in Human-Robot Interaction Author Guljelmović, Nikol (TU Delft Mechanical, Maritime and Materials Engineering) Contributor Jonker, P.P. (mentor) Kober, J. (mentor) Zeestraten, Martijn (mentor) Degree granting institution Delft University of Technology Programme Mechanical Engineering | Biomechanical Design - BioRobotics Date 2017-08-25 Abstract 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 Subject Task-parameterized movement representationTask parameter inferencehuman-robot interactionProcrustes analysis To reference this document use: http://resolver.tudelft.nl/uuid:059dc816-2edb-4d44-a841-cc4d58ecf802 Part of collection Student theses Document type master thesis Rights © 2017 Nikol Guljelmović Files PDF NGuljelmovic_MscThesis.pdf 11.13 MB Close viewer /islandora/object/uuid:059dc816-2edb-4d44-a841-cc4d58ecf802/datastream/OBJ/view