dc.title: Dynamic whole-body planning for humanoids in confined spaces : a morphology-aware synthesis approach dc.description.abstract: While bipedal locomotion on flat and structured terrains has seen significant progress through methods ranging from reduced-order models to learning-based methods, navigating confined and unstructured environments remains a critical challenge for humanoid robots. This dissertation advances the state-of-the-art in strategies for generating controlled, safe, and computationally efficient humanoid locomotion in these complex settings. At the planning level, I introduce a multi-stage whole-body planning framework that generates dynamically feasible motions while enforcing environment- and self-collision avoidance. This is done by strategically introducing convex relaxations to plan the paths of the robot's torso, hands, knees, and feet as a set of constrained particles. The resulting paths are then refined by including volume-aware and differentiable collision avoidance constraints that increase the reliability of these guiding paths. As a last step, dynamically feasible motions are efficiently found by focusing on motions near the aforementioned collision-free paths. This last step uses a two-pass optimization strategy to achieve dynamically, physically, and morphologically consistent motions. As Model Predictive Control often governs the mid-level control layer in locomotion, I present an Adaptive Horizon MPC strategy that maintains the performance of a long-horizon MPC with approximately half the computational effort by leveraging a Neural Network to approximate the solution to a Bilevel Optimization problem searching for the optimal prediction horizon in real time. Finally, this dissertation provides a suite of full-stack integration tools developed through my hardware bring-up work. While validated on a custom-made mobile platform, these tools are designed for generalizability to facilitate the rapid bring-up of complex robotic systems, including humanoids.