Most progress in humanoid control is driven by experiments on proprietary, closed-source systems. However, closed-source systems hamper community-driven development, make it difficult to modify or extend the hardware, and often lead to costly repairs that can cause critical delays. To address these issues, this thesis presents an open-source humanoid robot and demonstrates its capabilities on motion tracking and locomotion tasks. Our humanoid robot is compact, low-cost, and its 3D-printed components can be modified or extended with additional sensors or manipulation tools. We create an accurate simulation model of the humanoid robot, including identifying actuator parameters, and use it to train policies for motion tracking and locomotion in simulation. For motion tracking, the robot follows a reference motion, either from a prerecorded motion sequence or in real time from pose estimates derived from webcam frames, whereas for locomotion, it follows a velocity command. For both tasks, the trained policies are transferred to the real robot without further fine-tuning, demonstrating the accuracy of the simulation model. We evaluate variants of motion-tracking policies and compare their performance. Furthermore, we quantitatively evaluate sim-to-real transfer for locomotion and observe a sim-to-real gap; yet the trained policy remains robust to this gap and successfully tracks velocity commands on the real robot.
