Skip to main content

Todd D. MurpheyProfessor of Mechanical Engineering

Professor Murphey's research focuses on computational methods in dynamics and control, with applications in neuroscience, health science, robotics, and automation. The group focuses on computational models of embedded control, biomechanical simulation, dynamic exploration, and hybrid control.  The mathematical approaches used by the group lead to many orders of magnitude improvement in computational efficiency for reliable real-time implementation. Applications include assistive exoskeleton control, stabilization of energy networks, bio-inspired active sensing, entertainment robots, robotic exploration, and software-enabled stroke rehabilitation. 

Curriculum vitae

Interactive & Emergent Autonomy Lab

In the Interactive and Emergent Autonomy Lab, our research focuses on computational methods in data-driven control, information theory in physical systems, and embodied intelligence. We investigate how both autonomous systems and biological systems interact with their environments (and, in some cases, with each other) to learn and improve their behaviors. This work often involves mathematical modeling, development of new mathematical tools, algorithmic implementation and programming, and experimentation.

Example projects include robotic exploration using electrosenserobotic exploration using mechanical contact, human-in-the-loop control, and shared control for rehabilitation/assistive devices. We also work in the field of algorithmic matter, where we develop computational models to enable the design of microrobots with minimal actuation, sensing, and computation.

Learn more about our projects.

Interactive & Emergent Autonomy Lab GitHub

California Institute of Technology Pasadena, CA
Ph.D. in Control and Dynamical Systems, 2002
Thesis: Control of Multiple Model Systems

University of Arizona Tucson, AZ
B.S. in Mathematics, summa cum laude, 1997

Professor Murphey has developed the ME 314 Machine Dynamics course, focusing on the application of variational analysis to simulation anddesign of mechanisms.  He has additionally developed ME 454, anintroduction to numerical methods in optimal control.  In 2013 he taught an online version of one of the Engineering Analysis courses as a Coursera Massive Open Online Course (MOOC) (more information can be found at Coursera class Everything is the Same: Modeling Engineered Systems).  In all these courses, Professor Murphey focuses on project-based learning.  He has been a featured speaker at the National Academy of Engineering Frontiers of Engineering Education Workshop (see his blog here).

Science Robotics

Katie's paper Ergodicity reveals assistance and learning from physical human-robot interaction was published in Science Robotics in April 2019. In the paper, information theoretic principles were applied to the investigation of physical human-robot interaction.

NSF Science Nation

Engineering highly adaptable robots requires new tools for new rules. NSF-funded research aiming to make it easier for humans to work directly with a robotic partner in applications such as physical therapy.

Control Systems Society

Ahalya won third place in the 2017 IEEE Control Systems Society Video Clip Contest with her video Autonomous Robot Drawing: From Distribution to Actions using Feedback.

CBS Chicago

Professor Murphey was interviewed as part of a CBS story on robots improving productivity.

Back to top