Date of Award
2023
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Mechanical and Aerospace Engineering
Committee Chair
Farbod Fahimi
Committee Member
Yuri Shtessel
Committee Member
Emil Jovanov
Committee Member
Gang Wang
Committee Member
Chang-kwon Kang
Subject(s)
Androids--Control, Bipedalism, Gait in humans--Simulation methods, Reinforcement learning
Abstract
This dissertation examines the use of model-free control methods in various aspects of effective balance and gait control for two-legged humanoid robots. It explores what it should mean to improve balance and gait control for robots and proposes gait fragility as an alternative framework to the prevailing yet undefined dynamic stability objective. Next, it presents techniques for trajectory tracking control without deriving dynamic models, using modifications of the conventional reinforcement learning approach. Finally, it explores the use of deep online reinforcement learning with disturbance and assistance curricula for learning standing push recovery in bipeds.
Recommended Citation
Sweafford, Jerry Jr., "Model-free control methods for gait and standing push recovery in bipedal humanoid robots" (2023). Dissertations. 372.
https://louis.uah.edu/uah-dissertations/372