Title: Enabling Scalable, Robust, and Versatile Exoskeleton Control Using Deep Learning and Transfer Learning Methods    Date: Tuesday, November 15th Time: 9AM EST Location: GTMI Auditorium (Room 101)   Keaton Scherpereel Robotics PhD Student School of Mechanical Engineering Georgia Institute of Technology   Committee: Dr. Aaron Young (Advisor) – School of Mechanical Engineering, Georgia Institute of Technology Dr. Omer Inan (Advisor) – School of Electrical and Computer Engineering, Georgia Institute of Technology Dr. Matthew Gombolay – School of Interactive Computing, Georgia Institute of Technology Dr. Thomas Ploetz – School of Interactive Computing, Georgia Institute of Technology Dr. Gregory Sawicki – School of Mechanical Engineering, Georgia Institute of Technology   Abstract: Current state-of-the-art exoskeleton control is tailored to aid users in only one specific task or a finite number of tasks, but this task specificity hampers real-world application due to the variable and sporadic nature of human movement. A novel and emerging solution to this inherent drawback is to use deep learning models with inputs from wearable sensors to directly estimate user’s internal biological joint moment. This provides a continuous, task-agnostic signal upon which to build an assistance profile. To accelerate the advancement of this new approach, unique sensing modalities must be explored to better capture internal physiological states (Aim 1), additional data must be leveraged to improve user and task generalizability (Aim 2), and a path must be established for scaling this control architecture to novel devices (Aim 3). These advancements will be a useful step in propelling exoskeleton technologies beyond laboratory testing to real-world applications and contribute to scientific knowledge in exoskeleton control and applied machine learning.