Faculty Directory
Todd Murphey

Professor of Mechanical Engineering

Director of the Master of Science in Robotics Program

Director of Transformative Research

Contact

2145 Sheridan Road
Tech B286
Evanston, IL 60208-3109

847-467-1041Email Todd Murphey

Website

Center for Robotics and Biosystems

Departments

Mechanical Engineering

Affiliations

Master of Science in Robotics Program

Download CV

Education

Ph.D. Control and Dynamical Systems, California Institute of Technology, Pasadena, CA

B.S. Mathematics (summa cum laude), University of Arizona, Tucson, AZ


Research Interests

Professor Murphey's research focuses on computational methods in data-driven control, information theory in physical systems, and embodied intelligence. Example projects include robotic exploration using mechanical contact, human-in-the-loop control, and shared control.  


Significant Recognition

  • National Science Foundation CAREER award (2006)
  • Defense Science Study Group 2014-2015

Significant Professional Service

  • Editor for IEEE Transactions on Robotics (2014-2018)
  • Member: Air Force Scientific Advisory Board (2019-present)

Selected Publications

T. Berrueta, A. Pinosky, and T. D. Murphey, “Maximum diffusion reinforcement learning,” Nature Machine Intelligence, vol. 6, no. 5, pp. 504–515, 2024.


A. T. Liu, M. Hempel, J. F. Yang, A. M. Brooks, A. Pervan, V. B. Koman, G. Zhang, D. Kozawa, S. Yang, D. Goldman, M. Z. Miskin, A. W. Richa, D. Randall, T. D. Murphey, T. Palacios, and M. S. Strano, “Colloidal robotics,” Nature Materials, 2023.


J. Meyer, A. Prabhakar, A. Pinosky, I. Abraham, A. Taylor, M. Schlafly, K. Popovic, G. Diniz, B. Teich, B. Simidchieva, S. Clark, and T. D. Murphey, “Scale-invariant specifications for human-swarm systems,” IEEE Transactions on Field Robotics, vol. 1, pp. 276–291, 2024.


A. Pinosky, I. Abraham, A. Broad, B. Argall, and T. D. Murphey, “Hybrid control for combining model-based and model-free reinforcement learning,” International Journal of Robotics Research, vol. 42, no. 6, pp. 337–355, 2023.


G. Mamakoukas, I. Abraham, and T. D. Murphey, “Learning stable models for prediction and control,” IEEE Transactions on Robotics, vol. 39, no. 3, pp. 2255–2275, 2023.


A. Kalinowska, P. M. Pilarski, and T. D. Murphey, “Embodied communication: How robots and people communicate through physical interaction,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 6, pp. 205–232, 2023.


J. F. Yang, T. A. Berrueta, A. M. Brooks, A. T. Liu, G. Zhang, D. Gonzalez-Medrano, S. Yang, V. B. Koman, P. Chvykov, L. N. LeMar, M. Z. Miskin, T. D. Murphey, and M. S. Strano, “Emergent microrobotic oscillators via asymmetry-induced order,” Nature Communications, vol. 13, p. 5734, 2022.


A. Prabhakar and T. D. Murphey, “Mechanical intelligence for learning embodied sensor-object relationships,” Nature Communications, vol. 13, p. 4108, 2022.


H. Yasuda, P. R. Buskohl, A. Gillman, T. D. Murphey, S. Stepney, R. A. Vaia, and J. R. Raney, “Mechanical computing,” Nature, vol. 598, pp. 39–48, 2021.


A. Taylor, T. Berrueta, and T. D. Murphey, “Active learning in robotics: A review of control principles,” Mechatronics, vol. 77, p. 102576, 2021.


G. Mamakoukas, M. Castano, X. Tan, and T. D. Murphey, “Derivative-based Koopman operators for real-time control of robotic systems,” IEEE Transactions on Robotics, vol. 37, no. 6, pp. 2173–2192, 2021.


P. Chvykov, T. Berrueta, A. Vardhan, W. Savoie, A. Samland, T. D. Murphey, K. Wiesenfeld, D. I. Goldman, and J. L. England, “Low rattling: A predictive principle for self-organization in active collectives,” Science, vol. 371, no. 6524, pp. 90–95, 2021.

In the Classroom

Professor Murphey developed the Coursera Massive Open Online Course (MOOC) "Everything Is the Same: Modeling Engineered Systems" and had over 18,000 students enroll in Autumn, 2013. The course was based on one of the core undergraduate classes in systems analysis (EA3), a class he has innovated through development of classroom experiments. Moreover, he has developed the ME 314 Machine Dynamics course, focusing on the application of variational analysis to simulation and design of mechanisms. He has additionally developed ME 454, an introduction to numerical methods in optimal control. In all these courses, Professor Murphey focuses on project-based learning.