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Hierarchical Optimization-Based Control for Dynamic Loco-Manipulation on Humanoid Robots
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Hierarchical Optimization-Based Control for Dynamic Loco-Manipulation on Humanoid Robots
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Dissertation
Hierarchical Optimization-Based Control for Dynamic Loco-Manipulation on Humanoid Robots
2025
Overview
Recent advancements in robotics have spurred vast interest in the development and investigation of practical applications of humanoid robots. To harness the full potential of these robots, particularly in terms of dynamic and robust motions, reliable motion control schemes are essential. This thesis presents the design, development, and implementation of hierarchical Model-Predictive Control (MPC)-based control strategies on humanoid robot Locomotion and Manipulation (Loco-manipulation), demonstrated on our in-house designed mini-humanoid robot series, Humanoids for Enhanced ConTrol and Open-source Research (HECTOR). The investigated control approaches delve into two major directions. First, we explore hierarchical optimization-based control frameworks for dynamic motion generation and control on bipedal humanoid robots, with a focus on adaptive-frequency strategies at both the MPC and periodic gait levels with careful model design, to achieve adaptive discrete terrain locomotion and robust push-recovery. Next, we dive into the hierarchical optimization-based control frameworks with Reduced-order Modeling (ROM) for humanoid loco-manipulation. We examine the progression from Single Rigid-body Models (SRBM) to kino-dynamic representations that account for both the robot and the manipulated objects, to push the limit of efficient whole-body loco-manipulation. Lastly, combining the lessons learned from the two directions of investigation, we extend the adaptive frequency feature in hierarchical optimization-based control through data-driven augmentations to model the complex gait frequency decision-making process for humanoid loco-manipulation. We provide thorough numerical and hardware validations and examinations of the proposed hierarchical optimization-based control frameworks.
Publisher
ProQuest Dissertations & Theses
Subject
ISBN
9798297604681
