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Martin Jagersand

Professor

University of Alberta

Martin Jagersand has worked 18 years in space robotics projects, at the German Aerospace Research Institute DLR, and the Canadian Space Agency. He has an MSc in Engineering Physics from Chalmers Sweden, and he received a US Senate Fulbright scholarship for his MSc and PhD studies in Computer Science at the University of Rochester, USA. He held a US National Science Foundation (NSF) CISE postdoc fellowship at Yale University, and then was a research faculty in the NSF Engineering Research Center for Surgical Systems and Technology at Johns Hopkins University. He is now a faculty member at the University of Alberta, where his research group has participated in the KUKA robot innovation challenge 2017-2018, as were one of six academic teams worldwide to reach finals placements, and demonstrated their robotics competition entry live at the Hannover Messe, the world's largest robotics and automation exhibit. He also participated in the 2014-15 Amazon robot picking challenge, reached finals placement and demonstrated in Seattle.

Martin Jagersand's Website

Sessions Martin Jagersand is a part of

Thursday, June 9, 2022

How are we integrating the advancements of AI and robotics into everyday life?

3:50 pm to 5:05 pm
in Salon 10

Talk Description

Expanding robotics from structured mass manufacturing into natural unstructured environments with applications relevant to Alberta in industry and health care

Robotics has revolutionized mass manufacturing, but is only now moving into Alberta's main sectors, natural resources, construction, logistics and health care. I will talk about robotics projects in these areas at Total SA, ACQBUILT, Amazon, KUKA, Kinova and Intuitive Surgical. I'll describe what robotics currently does well: autonomous mass manufacturing, where about three million robot arms worldwide mass produce items such as IKEA Billy shelves, phones, TVs, computers and cars. However robotics struggles when materials are not rigid (e.g. textiles) and dimensions not mm precise (e.g. wood studs). While millions of robots are deployed worldwide in specialty-built manufacturing assembly lines, only a few hundreds are deployed in unstructured environments and tasks such as robot surgery, human assistance and space robotics. The difference here is that tasks and motions do not precisely repeat, and currently these robots are directly controlled by a human either in tele-operation, where the human joysticks the robot or separate programming of each task. Better sensing - vision and touch, environment understanding from this sensing, as well as learning motions and tasks from human demonstration instead of programming, makes robotics more flexible. I will outline an approach that blends learning from humans, semi-autonomy and human collaboration on tasks that is adaptable to function in regular human environments and quick to deploy.

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