Exoskeleton and Prosthetic Intelligent Controls (EPIC) Lab


Education

  • Postdoctoral Fellow, Michigan University, 2014-2016
  • Ph.D., Northwestern University, 2014
  • M.S., Northwestern University, 2011
  • B.S., Purdue University, 2009

Teaching Interests

Professor Young's teaching interests encompass core mechanical engineering subjects, including robotics, automation and control at both undergraduate and graduate levels. His specialty is a graduate level course in biomechatronics of wearable robotic systems.

Research Interests

Professor Young is interested in researching wearable robotic technology to improve mobility in individuals with walking disability. This may include limb loss, stroke, cerebral palsy, advanced age/arthritis, spinal cord injury and other neurological diseases. For many of these individuals, community mobility is greatly impaired which has implications in long term health, economics (such as the ability to work a job), independence and quality of life. Additionally, we are interested in using exoskeleton technology to help augment human capability such as for industrial worker safety and efficacy during heavy tooling, construction, and/or lifting.  To address these critical societal needs, Professor Young works on understanding the science of how robotic technologies can physically work with human benefactors to improve clinical outcomes and mobility. Specifically, Professor Young’s research focuses on the control system which couples the device to the user. By using new artificial intelligence and machine learning techniques, his group is pioneering strategies to greatly enhance the human experience with wearable robotic technology. These technologies have the potential to increase independence and community mobility, reduce caretaker burden, and ultimately improve the quality of life in individuals suffering from lower limb disability.

Recent Publications

  • KL Scherpereel, MC Gombolay, MK Shepherd, CA Carrasquillo, OT Inan, ..., Deep domain adaptation eliminates costly data required for task-agnostic wearable robotic control, Science Robotics 10 (108), eads8652, 2025
  • Dean D. Molinaro, Keaton L. Scherpereel, Ethan Schonhaut, Georgios Evangelopoulos, Max K. Shepherd, Aaron Young, “Task-Agnostic Exoskeleton Control via Biological Joint Moment Estimation,” Nature, Volume 635, (2024), pp. 337-344.
  • Dawit Lee, Sanghuyub Lee, Aaron Young, “AI-Driven Universal Lower-Limb Exoskeleton System for Community Ambulation,” Science Advances, Volume 10, Issue 51 (2024) pp. eadq0288. 
  • Dean Molinaro, Inseung Kang, Aaron Young, “Estimating Human Joint Moments Unifies Exoskeleton Control and Reduces User Effort”, Science Robotics, Volume 9, Issue 8, (2024), pp. eadi8852. 
  • Inseung Kang, Dean Molinaro, Dongho Park, Dawit Lee, Pratik Kunapuli, Kinsey Herrin, Aaron Young, “Online adaptation framework enables personalization of exoskeleton assistance during neurologically impaired locomotion,” IEEE Transactions on Robotics, in-press, 2025.