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


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.


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.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Bipedal Robotics, Human-Robot Interaction, Assistive Devices, Hybrid System Theory, Preference-Based Learning
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Mechanical Engineering
Awards:Centennial Prize for the Best Thesis in Mechanical and Civil Engineering, 2023.
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Ames, Aaron D.
Thesis Committee:
  • Burdick, Joel Wakeman (chair)
  • Ames, Aaron D.
  • Murray, Richard M.
  • Yue, Yisong
Defense Date:12 May 2023
Non-Caltech Author Email:tucker.maegan (AT)
Record Number:CaltechTHESIS:04292023-003436131
Persistent URL:
Related URLs:
URLURL TypeDescription preference-based learning for safety-critical control: Article adapted for Chapter IV controller gains on bipedal walking robots via user preferences: Article adapted for Chapter IV stability in probability: Article adapted for Chapter II review of current state-of-the-art control methods for lower-limb powered prostheses: Article referenced in Chapter I variable assistance for lower body exoskeletons: Article adapted for Chapter V of safety and performance of the self balancing walking system Atalante in patients with complete motor spinal cord injury: Article referenced in Chapter I Region of interest active learning for characterizing exoskeleton gait preference landscapes: Article adapted for Chapter III multicontact walking for robotic assistive devices via musculoskeletal models and hybrid zero dynamics: Article adapted for Chapter V input-to-state stability perspective on robust locomotion: Article adapted for Chapter II preference-based learning for high-dimensional optimization of exoskeleton walking gaits: Article adapted for Chapter III learning for exoskeleton gait optimization: Article adapted for Chapter III learning for user-guided HZD gait generation on bipedal walking robots: Article adapted for Chapter IV bipedal locomotion: Leveraging saltation matrices for gait optimization: Article adapted for Chapter II
Tucker, Maegan Lindsay0000-0001-7363-6809
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:15149
Deposited By: Maegan Tucker
Deposited On:03 Jun 2023 01:47
Last Modified:16 Jun 2023 16:32

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