A dominant trend in manufacturing is the move toward small production volumes and high product variability. It is thus anticipated that future manufacturing automation systems will be characterized by a high degree of autonomy, and must be able to learn new behaviors without explicit programming. Robot Learning, and more generic, Autonomous Manufacturing, is an exciting research field at the intersection of Machine Learning and Automation. The combination of "traditional" control techniques with data-driven algorithms holds the promise of allowing robots to learn new behaviors through experience. This talk introduces selected Siemens research projects in the area of Autonomous Manufacturing.
Biography: Dr. Eugen Solowjow is a Research Scientist at Siemens’ central research division, Corporate Technology, located in Berkeley, CA, USA. His research interests cover topics in the area of Machine Autonomy, e.g. robot learning, visual and haptic perception, model-based-and data-driven controls, as well as Edge and Embedded AI. Eugen serves as PI and PM for Siemens internal and US Government funded research. Prior to joining Siemens, he received a Ph.D. from TU Hamburg and was a visiting scholar at U.C. Berkeley.