You don't use function calling. You specifically train the neural network to directly encode the robot action as a token. There are many ways. You can output absolute positions, delta positions, relative trajectory. You can do this in joint space or end effector space.

200Hz is barely enough to control a motor, but it is good enough to send a reference signal to a motor controller. Usually what is done is that you have a neural network to learn complex high level behaviour and use that to produce a high level trajectory, then you have a whole body robot controller based on quadratic programming that does things like balancing, maintaining contacts when holding objects or pressing against things. This requires a model of the robot dynamics so that you know the relationship between torques and acceleration. Then after that you will need a motor controller that accepts reference acceleration/torque, velocity and position commands which then is turned into 10kHz to 100kHz pulse width modulated signals by the motor controller. The motor controller itself is driving MOSFETs so it can only turn them on or off, unless you are using expensive sinusoidal drivers.