Interactive & Emergent Autonomy Lab

Ola Kalinowska


I'm interested in designing algorithms for effective, safe, and intuitive robotics. My research thus far has focused on combining machine learning, Bayesian statistics, and control theory for human-oriented application domains, such as assistive and rehabilitation robotics. I've developed methods for safe shared control of human-machine systems, as well as explored learning-based solutions for improving a robot's understanding of human intent. In my work, I'm also interested in using robotics to discover underlying principles of human learning to then incorporate them into designing shared-autonomy interfaces and robot-aided rehabilitation.


Robot-Assisted Rehabilitation
Software Enabled Biomedical Devices


M.S. Mechanical Engineering, Northwestern University, 2018
B.S. Mechanical Engineering, Massachusetts Institute of Technology, 2016


TA for NUvention: Medical Innovation (ENTREP 470)
     Fall 2017-Winter 2018, Fall 2018-Winter 2019, Fall 2019
TA for Machine Dynamics (ME 314), Fall 2018

Other Interests

Skiing, sailing, hiking, volleyball, and being outdoors


Task-based hybrid shared control for training through forceful interaction
K. Fitzsimons, A. Kalinowska, J. Dewald and T. D. Murphey
International Journal of Robotics Research, 2020. PDF

Shoulder abduction loading affects motor coordination in individuals with chronic stroke, informing targeted rehabiliation
A. Kalinowska, K. Rudy, M. Schlafly, K. Fitzsimons, J. Dewald and T. D. Murphey
IEEE RAS/EMBS Int. Conf. on Biomedical Robotics and Biomechatronics (BioRob), 2020. PDF

Data-driven gait segmentation for walking assistance in a lower-limb assistive device
A. Kalinowska, T. Berrueta, A. Zoss, and T. D. Murphey
IEEE Int. Conf. on Robotics and Automation (ICRA), 2019. PDF

Online user assessment for minimal intervention during task-based robotic assistance
A. Kalinowska, K. Fitzsimons, J. Dewald, and T. D. Murphey
Robotics: Science and Systems Proceedings, 2018. PDF