{"id":12252,"date":"2019-07-16T11:42:22","date_gmt":"2019-07-16T11:42:22","guid":{"rendered":"https:\/\/www.profactor.at\/?page_id=12252"},"modified":"2023-08-11T07:01:45","modified_gmt":"2023-08-11T06:01:45","slug":"teachbots","status":"publish","type":"page","link":"https:\/\/www.profactor.at\/en\/research\/industrial-automation-systems\/robotics\/projects-archive\/teachbots\/","title":{"rendered":"TeachBots"},"content":{"rendered":"

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<\/div>
<\/div><\/div>[\/vc_column_text][vc_column_text]The aim of the project is to enable non-experts with the opportunity to easily teach robots a complex assembly process using natural communication methods. In order for robot<\/strong> systems to learn from a non-expert user, the robot system should first understand the user’s intention. In this project, human intentions<\/strong> are understood both through verbal communication and by observing human actions. In order to recognize human intentions through voice communication, PROFACTOR will work together with LIFEtool on the development of a new communication interface for robots. LIFEtool, with its extensive knowledge of online and offline speech recognition technologies, will support PROFACTOR in developing an interface to communicate human intentions with the robot<\/strong>. PROFACTOR will develop a “portable” activity detection system capable of detecting human actions using low-cost sensors. A feasibility study will also be conducted in the process to partially validate the applicability of such activity\/gesture recognition system to other domains (health care). The advantage of such a module would be its applicability to different scenarios requiring the understanding of human intentions.<\/p>\n

The strategy to achieve the goal that non-experts can easily teach robot systems<\/strong> is twofold. First, a ‘learning by interaction’ framework is developed in which the robot provides the user with a set of intelligent suggestions during the learning process. The robot uses its knowledge modeling and argumentation skills and takes into account the “current situation” (detected with the activity detection system) of the assembly environment to make these suggestions. A ‘learning by instruction’ framework is then developed that allows the use of ‘Natural Language’ as a communication mode between the user and the robot. Both frameworks are then combined into a bidirectional communication channel between the user and the robot in order to provide feedback or to re-learn (in whole or in part) the assembly process.<\/p>\n

Project name:<\/strong>
\n<\/strong>BRIDGE \u2013 HUMAN ROBOT INTERACTION (TEACHBOTS)<\/p>\n

Industrial Project<\/strong><\/p>\n

Duration:<\/strong>
\n<\/strong>04.2019 \u2013 12.2020<\/p>\n

Partners:<\/strong><\/p>\n

Profactor GmbH<\/p>\n

LIFEtool GmbH<\/p>\n

Keywords: Human Robot Collaboration; Programming by Interaction; Programming by Instruction; Artificial Intelligence<\/strong>[\/vc_column_text][\/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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Your contact<\/h4>\n

Akkaladevi Sharath Chandra\u00a0<\/strong>
\nScientist
\nRobotics and Assistive Systems<\/p>\n

+43 72 52 885 325
\nSharath.Akkaladevi@null<\/span>profactor.at<\/p>\n<\/div>\n<\/div>\n

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Wir beantworten …<\/h3>\n

… Ihre Fragen<\/a>[\/vc_column_text][\/vc_column][\/vc_row]<\/p>\n