Aaron Johnson

Short CV

Aaron M. Johnson is a Professor of Mechanical Engineering at Carnegie Mellon University, with additional appointments in the Robotics Institute and Electrical & Computer Engineering departments. He received his PhD in Electrical and Systems Engineering from the University of Pennsylvania in 2014 and his BS in Electrical and Computer Engineering from Carnegie Mellon University in 2008. His research interests are in hybrid systems, state estimation, control, learning, legged robots, and field robotics. He is an ASME Fellow and the recipient of the NSF Career award, the ARO Young Investigator Award, and the Best Paper award at the ICRA Workshop on Legged Robots, among other awards. 

Selected Awards

• 2025, ASME Fellow, American Association of Mechanical Engineers
• 2025, Hans Fischer Fellow, TUM Institute for Advanced Study
• 2023, Best Poster Award, IEEE Intl. Symposium on Multi-Robot and Multi-Agent Systems
• 2022-2025, Dean’s Early Career Fellow, Carnegie Mellon University, College of Engineering
• 2022, Best Workshop Paper, ICRA Workshop on Legged Robots
• 2021, George Tallman Ladd Research Award, Carnegie Mellon University, College of Engineering
• 2020, CAREER Award, National Science Foundation
• 2018, Young Investigator Award, Army Research Office
• 2008, David Tuma Laboratory Project Award, Carnegie Mellon University, ECE Dept

Research Interests

Robotics, Legged Robots, Field Robotics, Robot Design, Control Systems, Hybrid Dynamical Systems, Machine Learning

Selected Publications

• Nathan J. Kong, J. Joe Payne, James Zhu, and Aaron M. Johnson, “Saltation matrices: The essential tool for linearizing hybrid dynamical systems,” Proceedings of the IEEE, vol. 112, pp. 585–608, 6 2024
• Sean J. Wang, Honghao Zhu, and Aaron M. Johnson, “Pay attention to how you drive: Safe and adaptive model-based reinforcement learning for off-road driving,” in IEEE Intl. Conference on Robotics and Automation, 2024, pp. 16 954–16 960
• Ardalan Tajbakhsh, Lorenz T Biegler, and Aaron M. Johnson, “Conflict-based model predictive control for scalable multi-robot motion planning,” in IEEE Intl. Conference on Robotics and Automation, 2024, pp. 14 562–14 568
• Paul Nadan, Spencer Backus, and Aaron M. Johnson, “LORIS: A lightweight free-climbing robot for extreme terrain exploration,” in IEEE Intl. Conference on Robotics and Automation, 2024, pp. 18 480–18 486
• Catherine Pavlov and Aaron M. Johnson, “A terramechanics model for high slip angle and skid with prediction of wheel-soil interaction geometry,” Journal of Terramechanics, vol. 111, pp. 9–19, 2024
• Nathan Kong, Chuanzheng Li, George Council, and Aaron M. Johnson, “Hybrid iLQR model predictive control for contact implicit stabilization on legged robots,” IEEE Transactions on Robotics, vol. 39, no. 6, pp. 4712–4727, 2023
• James Kyle, Justin K. Yim, Kendall Hart, Sarah Bergbreiter, and Aaron M. Johnson, “The simplest walking robot: A bipedal robot with one actuator and two rigid bodies,” in IEEE-RAS International Conference on Humanoid Robots, 2023
• Justin K. Yim, Jiming Ren, David Ologan, Selvin Garcia Gonzalez, and Aaron M. Johnson, “Proprioception and reaction for walking among entanglements,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2023, pp. 2760–2767
• Nathan J. Kong, J. Joe Payne, George Council, and Aaron M. Johnson, “The Salted Kalman Filter: Kalman filtering on hybrid dynamical systems,” Automatica, vol. 131, p. 109 752, 2021
• Joseph C. Norby, Jun Yang Li, Cameron C. Selby, Amir Patel, and Aaron M. Johnson, “Enabling dynamic behaviors with aerodynamic drag in lightweight tails,” IEEE Transactions on Robotics, vol. 37, no. 4, pp. 1144–1153, 2021