{"id":8365,"date":"2017-11-02T10:19:03","date_gmt":"2017-11-02T10:19:03","guid":{"rendered":"https:\/\/www.profactor.at\/?page_id=8365\/"},"modified":"2023-08-11T07:01:31","modified_gmt":"2023-08-11T06:01:31","slug":"lern4mrk","status":"publish","type":"page","link":"https:\/\/www.profactor.at\/en\/research\/industrial-automation-systems\/robotics\/projects-archive\/lern4mrk\/","title":{"rendered":"Lern4MRK"},"content":{"rendered":"

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<\/div><\/div>[\/vc_column_text][vc_column_text]Collaborative lightweight robots are a trend in industry. They are comparatively cheap. Developments in the field of machine learning make them increasingly flexible and easier to use. The challenges for research are obvious. The machines must be equipped with cognitive intelligence in order to adapt to a changed environment and understand new tasks. The aims of the project go far beyond the state of the art of research.<\/p>\n

The focus is on “transfer learning”: from manual human activities to the robot and from a product or process variant to a similar one.<\/p>\n

The main research objectives of the project are<\/p>\n

1. mapping of human motion to the robot
\n2. the “understanding” of temporal task correlations and process parameters by the robot
\n3. adaptability to similar processes with as few new examples as possible<\/p>\n

No external cooperation is envisaged for the implementation of the project.<\/p>\n

Project name:<\/strong>
\nLERN4MRK: Modellieren, Erlernen und Abstrahieren von Prozessen f\u00fcr die Mensch-Roboter Kooperation<\/p>\n

Funding:<\/strong>
\nbmvit<\/p>\n

Duration:\u00a0\u00a0<\/strong>
\n01.07.2017 \u2013 30.06.2021[\/vc_column_text][vc_tta_accordion active_section=”1″ title=”Publikationen”][vc_tta_section title=”Reviewed publications in Journals” tab_id=”1509626591358-8ffc3fec-7d23″][vc_column_text]S.C. Akkaladevi, M. Plasch, and A. Pichler, “Skill-based learning of an assembly process” Elektrotech. Inftech. (2017) 134: 312, Springer Vienna. https:\/\/doi.org\/10.1007\/s00502-017-0514-2\u00a0<\/a>[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”Reviewed publications in conference proceedings ” tab_id=”1509618286472-3049b1f8-04ca”][vc_column_text]C. Heindl, T. Poenitz, G. Stuebl, A. Pichler, and J. Scharinger, \u201cSpatio-thermal depth correction of RGB-D sensors based on Gaussian Processes in real-time\u201d in The 10th International Conference on Machine Vision, to be published, 2017.[\/vc_column_text][\/vc_tta_section][\/vc_tta_accordion][\/vc_column][vc_column width=”1\/4″ el_class=”page-sidebar page-reference-sidebar”][vc_column_text el_class=”reference-person”]<\/p>\n

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\n

Your contact<\/h4>\n

DI Dr. Gernot St\u00fcbl<\/strong>
\nScientist
\nRobotics and Assistive Systems<\/p>\n

+43 72 52 885 313
\ngernot.stuebl@null<\/span>profactor.at<\/p>\n<\/div>\n

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We answer …<\/h3>\n

… your questions<\/a>[\/vc_column_text][\/vc_column][\/vc_row]<\/p>\n