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PhD Defense by Keaton Scherpereel
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Title: Enabling Scalable, Versatile, and Robust Control for Robotic Exoskeletons
Date: Tuesday, April 23rd
Time: 1PM EST
Location: Manufacturing Related Disciplines Complex (MRDC) 4211
Zoom link: https://gatech.zoom.us/j/95274057500
Keaton Scherpereel
Robotics PhD Candidate
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. Gregory Sawicki – School of Mechanical 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
Abstract:
Lower-limb exoskeleton technologies—rigid or soft devices that provide assistance to users—show promise in restoring and augmenting human movement. However, current state-of-the-art exoskeleton control primarily addresses consistent, time-repeatable tasks and device-specific, state-machine-based transitions that stand in stark contrast with the fluidity and variability of natural human movement. As I demonstrate in this work, even at its theoretical best, the current control paradigm cannot handle the uncertain and ever-changing environment we live in. In this work, I expand controllers based on deep learning estimates of physiological state to operate in the expansive regime of human activities while also generalizing to novel activities. I show that, when deployed on a hip and knee exoskeleton, these controllers can augment human performance across tasks and time-varying conditions, promising task-agnostic and user-independent control. The process of training these models, however, is device-specific and highly costly in terms of resources and personnel. This threatens to negate its potential for real-world viability. In this work, I also present a novel framework that uses deep domain adaptation to reduce or eliminate the need for costly device-specific data. When deployed on an exoskeleton in real-time, these data-limited models still achieved performance comparable to models with complete access to costly data. These advances are a promising step toward enabling exoskeletons to break the critical task- and device-specific barriers to everyday, outside-laboratory use, and thereby achieve their transformative potential to aid ordinary people.
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- Workflow Status:Published
- Created By:Tatianna Richardson
- Created:04/09/2024
- Modified By:Tatianna Richardson
- Modified:04/09/2024
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