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We are developing a self-learning mechatronic golf robot using combineddata-driven and physics-based methods, to have the robot autonomously learn toputt the ball from an arbitrary point on the green. Apart from the mechatroniccontrol design of the robot, this task is accomplished by a camera system withimage recognition and a neural network for predicting the stroke velocityvector required for a successful hole-in-one. To minimize the number oftime-consuming interactions with the real system, the neural network ispretrained by evaluating basic physical laws on a model, which approximates thegolf ball dynamics on the green surface in a data-driven manner. Thus, wedemonstrate the synergetic combination of data-driven and physics-based methodson the golf robot as a mechatronic example system.

Autonomous Golf Putting with Data-Driven and Physics-Based Methods