Faculty Directory
Todd Murphey

Professor of Mechanical Engineering

Contact

2145 Sheridan Road
Tech B286
Evanston, IL 60208-3109

847-467-1041Email Todd Murphey

Website

Neuroscience and Robotics Lab (NxR)


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 electrosense, robotic exploration using mechanical contact, human-in-the-loop control, and shared control for rehabilitation/assistive devices.  


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

    C. Chen, T. D. Murphey, and M. A. MacIver, “Tuning movement for sensing in an uncertain world,” eLife, vol. 9, p. e52371, 2020.

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

    T. Fan, H. Wang, M. Rubenstein, and T. D. Murphey, “CPL-SLAM: Efficient and certifiably correct planar graph-based SLAM using the complex number representation,” IEEE Transactions on Robotics, 2020.

    A. Broad, I. Abraham, T. Murphey, and B. Argall, “Data-driven Koopman operators for model-based shared control of human-machine systems,” International Journal of Robotics Research, 2020.

    I. Abraham, A. Handa, N. Ratliff, K. Lowrey, T. Murphey, and D. Fox, “Model-based generalization under parameter uncertainty using path integral control,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 2864–2871, 2020.

    W. Savoie, T. A. Berrueta, Z. Jackson, A. Pervan, R. Warkentin, S. Li, T. D. Murphey, K. Wiesenfeld, and D. I. Goldman, “A robot made of robots: emergent transport and control of a smarticle ensemble,” Science: Robotics, vol. 4, no. 34, p. eaax4316, 2019.

    K. Fitzsimons, A. M. Acosta, J. Dewald, and T. D. Murphey, “Ergodicity reveals assistance and learning in physical human robot interaction,” Science: Robotics, vol. 4, no. 29, p. eaav6079, 2019.

    I. Abraham and T. D. Murphey, “Active learning of dynamics for data-driven control using Koopman operators,” IEEE Transactions on Robotics, 2019. 2019 King-Sun Fu IEEE Transactions on Robotics Best Paper

    A. Prabhakar, I. Abraham, M. Schlafly, A. Taylor, K. Popovic, G. Diniz, B. Teich, B. Simidchieva, S. Clark, and T. Murphey, “Ergodic specifications for flexible swarm control: From user commands to persistent adaptation,” in Robotics: Science and Systems Proceedings, 2020.

    T. Berrueta, A. Pervan, K. Fitzsimons, and T. Murphey, “Dynamical system segmentation for information measures in motion,” IEEE Robotics and Automation Letters, 2019.

    E. Tzorakoleftherakis and T. D. Murphey, “Iterative sequential action control for stable, model-based control of nonlinear systems,” IEEE Transactions on Automatic Control, 2019.

    G. Mamakoukas, M. Maciver, and T. D. Murphey, “Feedback synthesis for underactuated systems using sequential second-order needle variations,” International Journal of Robotics Research, 2019.

    A. Broad, T. Murphey, and B. Argall, “Highly parallelized data-driven MPC for minimal intervention shared control,” in Robotics: Science and Systems Proceedings, 2019.

    G. Mamakoukas, M. Castano, X. Tan, and T. D. Murphey, “Local Koopman operators for data-driven control of robotic systems,” in Robotics: Science and Systems Proceedings, 2019.

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.