Prof. Sami Haddadin
Sami Haddadin is Director of the Munich School of Robotics and Machine Intelligence at the Technical University of Munich (TUM) and holds
the Chair of Robotics and Systems Intelligence. Prior to that he was Chair of the Institute of Automatic Control at Gottfried Wilhelm Leibniz
Universität Hannover and held various positions as a researcher at DLR. He holds degrees in electrical engineering, computer science and technology
management from the Technical University of Munich and the Ludwig Maximilian University of Munich. He received his PhD with summa cum laude from
RWTH Aachen University. His research interests include robot design and control, robot learning, collective intelligence, safe human-robot interaction,
human neuromechanics, and intelligent prosthetics. He has published more than 200 scientific articles in international journals and conferences.
He has received numerous national and international awards for his scientific work, including the IEEE Early Career Award, the RSS Early Career
Spotlight, the German Future Prize of the Federal President and the Leibniz Prize. He is a member of the national academy of science and engineering,
the High-Level Expert Group on AI of the European Commission and he is Chairman of the Bavarian AI-Council .
Title of talk:
Robots that learn and interact safely human environments
Enabling robots for safe interaction with humans and unknown environments while learning during these interactions has been one of the primary
goals of robotics research over decades. Recently significant leaps forward, including also breakthroughs in direct human-robot collaboration,
were made in particular in industrial robotics. Interactive and connected robot assistants are already able to follow Asimov’s first law – safely
interact with the human. However; the major challenges still remain in equipping robots with capabilities of autonomous learning of physical human-robot
interaction, continuously developing new skills and acting safely under any unknown situation. Specifically, human-centered robot design, tactile control
and physics grounded machine learning algorithms allow robots to acquire new capabilities and skills. This technology will transform traditional
manufacturing as we know it today, and start becoming relevant to professional service and domestic applications. Also, in the recent Corona crisis
it became clear how much tactile robotics technology could support our societal and health needs, as underlined by several applications I will showcase.