Concepts and algorithms for robot motion control are based on feedback control schemes that keep robots on their desired position or force trajectories. Going beyond foundational concepts like PID control in joint space, we explain how to design algorithmic concepts that are able to compensate for robot model errors and environmental uncertainties. For applications requiring high precision, high velocities, high stiffness, and high repeatability, position control methods are optimized for various use cases that we will discuss. Using application-specific input signals, robot path and trajectory planning algorithms provide set points for underlying motion controllers. We will learn how to design real-time path and trajectory planning algorithms that can be used in hybrid switched-system controllers or for human-robot collaboration applications. With the goal of planning more complex paths and trajectories, sampling-based methods, optimal planning concepts, and reinforcement learning algorithms are explained.