Dr. Metin Sitti is the director of the Physical Intelligence Department at the Max Planck Institute for Intelligent Systems in Stuttgart, Germany. He is also an honorary professor in University of Stuttgart, professor in Koç University, Istanbul, Turkey, and distinguished service professor in Carnegie Mellon University. He was a professor in the Department of Mechanical Engineering and in the Robotics Institute at Carnegie Mellon University, USA (2002-2014) and a research scientist at University of California at Berkeley, USA (1999-2002). He received the BSc (1992) and MSc (1994) degrees in electrical and electronics engineering from Boğaziçi University, Turkey, and the PhD degree (1999) in electrical engineering from the University of Tokyo, Japan.
He is an IEEE Fellow. As selected awards, he received the ERC Advanced Grant (2019), Rahmi Koç Medal of Science (2018), Best Paper Award in the Robotics Science and Systems Conference (2019), IEEE/ASME Best Mechatronics Paper Award (2014), SPIE Nanoengineering Pioneer Award (2011), Best Paper Award in the IEEE/RSJ Intelligent Robots and Systems Conference (1998, 2009), and NSF CAREER Award (2005). He has published over 440 peer-reviewed papers, over 240 of which have appeared in archival journals. He is the editor-in-chief of both Progress in Biomedical Engineering and Journal of Micro-Bio Robotics. His research interests include physical intelligence, small-scale mobile robotics, bio-inspiration, advanced functional micro/nanomaterials, and miniature medical devices.
We have developed a millimeter-scale magnetically driven swimming robot for untethered motion at mid to low Reynolds numbers. The robot is propelled by continuous undulatory deformation, which is enabled by the distributed magnetization profile of a flexible sheet. We demonstrate control of a prototype device and measure deformation and speed as a function of magnetic field strength and frequency. Experimental results are compared with simple magnetoelastic and fluid propulsion models. The presented mechanism provides an efficient remote actuation method at the millimeter scale that may be suitable for further scaling down in size for microrobotics applications in biotechnology and healthcare
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems