  We are developing a self-learning mechatronic golf robot using combined
data-driven and physics-based methods, to have the robot autonomously learn to
putt the ball from an arbitrary point on the green. Apart from the mechatronic
control design of the robot, this task is accomplished by a camera system with
image recognition and a neural network for predicting the stroke velocity
vector required for a successful hole-in-one. To minimize the number of
time-consuming interactions with the real system, the neural network is
pretrained by evaluating basic physical laws on a model, which approximates the
golf ball dynamics on the green surface in a data-driven manner. Thus, we
demonstrate the synergetic combination of data-driven and physics-based methods
on the golf robot as a mechatronic example system.
