Enabling Robust and User-Customized Bipedal Locomotion on Lower-Body Assistive Devices via Hybrid System Theory and Preference-Based Learning

Citation

Tucker, Maegan Lindsay (2023) Enabling Robust and User-Customized Bipedal Locomotion on Lower-Body Assistive Devices via Hybrid System Theory and Preference-Based Learning. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/j9hk-xa17. https://resolver.caltech.edu/CaltechTHESIS:04292023-003436131

Abstract

Practical robotic assistive devices have the potential to transform many aspects of our society, from enabling locomotive autonomy to facilitating rehabilitation. However, as is typically the case when having autonomous systems interact closely with humans, one must simultaneously solve multiple grand challenges. My work focuses specifically on 1) leveraging hybrid system theory to achieve stable and robust walking that generalizes well across various human models and environmental conditions, and 2) developing an online learning strategy to customize the experimental walking for individual user comfort. The presented methodology is grounded in realizing lower-body exoskeleton locomotion for subjects with motor complete paraplegia, with extensions to other robotic applications. The contributions are broken down as follows.

First, by leveraging tools from nonlinear control theory, I propose techniques for systematically addressing locomotive robustness. These techniques include: using saltation matrices to generate robust gaits with experimental demonstrations on the Atalante lower-body exoskeleton; and developing an input-to-state stability perspective to certify robustness to uncertain impact events. Importantly, these methods aim to better understand the mathematical conditions underlying robust locomotion -- a necessary step towards realizing safe locomotion across varying human models and environmental conditions. Second, I develop a preference-based learning framework to explicitly optimize user comfort during exoskeleton locomotion (achieved using the aforementioned nonlinear control methodology) by learning directly from subjective feedback. This framework is implemented in real-world settings, including the clinical realization of user-preferred locomotion for two subjects with motor complete paraplegia.Third, the extensibility of this framework is demonstrated through three general robotic applications: tuning constraints of the gait generation optimization problem with demonstrations on a planar biped; tuning Lyapunov-based controller gains on a 3D biped; and tuning control barrier function parameters for performant yet safe exploration on a quadrupedal platform. Lastly, I discuss other relevant clinical considerations for lower-body assistive devices including how exoskeleton locomotion influences metabolic cost of transport, the study of latent factors underlying user-preferred walking, and embedding musculoskeletal models directly in the gait generation process.

Thesis Files