This thesis comes at a time of unprecedented growth in humanoid robotics, driven by rapid technological advances and growing enthusiasm among private actors and the general public. In this context, recent progress in humanoid robot motor skills and their growing social acceptance seem to herald their imminent integration into real-world environments alongside humans. Faced with this ambition, a major challenge remains: ensuring the robustness and autonomy of locomotion in the dynamic contexts in which these robots will operate. To address this challenge, this work combines physical modeling and reinforcement learning, exploiting the complementary advantages of these two paradigms—the stability guarantees offered by modeling and the adaptability provided by learning. This manuscript begins with a state-of-the-art review of control and learning methods applied to humanoid locomotion, aimed at identifying the most promising approaches for reconciling robustness, adaptability, and dynamic realism. On this foundation, the PlaCo software, dedicated to motion planning and robot control, is developed. It aims to abstract the complexity of the optimization formulations necessary for trajectory generation, while maintaining performance compatible with real-time execution. This framework is then used to design and deploy a walking controller based on the linear inverted pendulum model (LIPM) on the Sigmaban humanoid robot. This development highlights the model's ability to produce consistent trajectories in real time, while revealing the practical limitations encountered on a real platform. In order to overcome these limitations and enable dynamic adaptation to disturbances, a reinforcement learning agent dedicated to fall recovery is developed. Trained in simulation, this agent is successfully transferred to the real robot, demonstrating a significant gain in autonomy. The difficulty of this transfer nevertheless highlights the central issue of the gap between simulated and real environments. This observation leads us to seek ways to minimize this discrepancy by improving the accuracy of the simulation. An in-depth study of friction phenomena in servo-actuators has been conducted, showing how a more detailed consideration of these phenomena improves the quality of the simulation and policy transfers.