Metin Sitti

Director

Physical Intelligence

+49 711 689-3423

http://nanolab.me.cmu.edu/members/

Overview
Publications

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.

490 results (View BibTeX file of all listed publications)

2020

Learning of sub-optimal gait controllers for magnetic walking soft millirobots

Culha, U., Demir, S. O., Trimpe, S., Sitti, M.

In Proceedings of Robotics: Science and Systems, July 2020, Culha and Demir are equally contributing authors (inproceedings)

Abstract

Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can access confined spaces in the human body. However, due to highly nonlinear soft continuum deformation kinematics, inherent stochastic variability during fabrication at the small scale, and lack of accurate models, the conventional control methods cannot be easily applied. Adaptivity of robot control is additionally crucial for medical operations, as operation environments show large variability, and robot materials may degrade or change over time,which would have deteriorating effects on the robot motion and task performance. Therefore, we propose using a probabilistic learning approach for millimeter-scale magnetic walking soft robots using Bayesian optimization (BO) and Gaussian processes (GPs). Our approach provides a data-efficient learning scheme to find controller parameters while optimizing the stride length performance of the walking soft millirobot robot within a small number of physical experiments. We demonstrate adaptation to fabrication variabilities in three different robots and to walking surfaces with different roughness. We also show an improvement in the learning performance by transferring the learning results of one robot to the others as prior information.

link (url) DOI [BibTex]

2020

pi Culha, U., Demir, S. O., Trimpe, S., Sitti, M. Learning of sub-optimal gait controllers for magnetic walking soft millirobots In Proceedings of Robotics: Science and Systems, July 2020, Culha and Demir are equally contributing authors (inproceedings)

link (url) DOI [BibTex]

SIMULTANEOUS CALIBRATION METHOD FOR MAGNETIC LOCALIZATION AND ACTUATION SYSTEMS

Sitti, M., Son, D., Dong, X.

June 2020, US Patent App. 16/696,605 (misc)

Abstract

The invention relates to a method of simultaneously calibrating magnetic actuation and sensing systems for a workspace, wherein the actuation system comprises a plurality of magnetic actuators and the sensing system comprises a plurality of magnetic sensors, wherein all the measured data is fed into a calibration model, wherein the calibration model is based on a sensor measurement model and a magnetic actuation model, and wherein a solution of the model parameters is found via a numerical solver order to calibrate both the actuation and sensing systems at the same time.

Metin Sitti

2020

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