Interactive & Emergent Autonomy Lab

Katie Fitzsimons


Robotics and haptics have the potential to enhance human performance and learning as well as provide unique insight into neuromotor function through sensing and quantification of human motion. At the same time, human behavior can inform the development of control strategies for complex tasks and human-robot interactions. The methods used for evaluation of motion greatly influences our ability to recognize the effects of assistance and training from a statistical standpoint, but more importantly, the mathematical structure imposed by unique measures of motion quality has significant impact on the algorithmic tools that are available to manage the interactions between robots and humans. My research aims to study alternatives to traditional measures of motion (e.g., energy or error) for quanitifying motion quality and synthesizing controls during physical human-robot interaction.


Robot-Assisted Rehabilitation
Software-Enabled Biomedical Devices


M.S. Mechanical Engineering, Northwestern University, 2017
B.S. Mechanical Engineering, Michigan State University, 2013

Awards and Honors

NDSEG Fellowship
NSF Graduate Research Fellowship


TA for Active Learning in Robotics (ME495), Spring 2018
Grader for Machine Dynamics (ME 314), Fall 2015, Fall 2016, Fall 2017, Fall 2018
Grader for Mechanics of Sports (ME 360) Spring 2017, Spring 2018


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

Ergodicity reveals assistance and learning in physical human robot interaction
K. Fitzsimons, A. M. Acosta, J. Dewald, and T. D. Murphey
Science Robotics, vol. 4, no. 29, 2019.
For a video and a freely accessible link to the paper, click here.

Dynamical system segmentation for information measures in motion
T. Berrueta, A. Pervan, K. Fitzsimons, and T. D. Murphey
IEEE Robotics and Automation Letters, vol. 4, no. 1, pp. 169–176, 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

Optimal human-in-the-loop interfaces based on Maxwell’s demon
K. Fitzsimons, E. Tzorakoleftherakis, and T. D. Murphey
American Controls Conf. (ACC), pp. 4397 – 4402, 2016. PDF