  Minigolf is an exemplary real-world game for examining embodied intelligence,
requiring challenging spatial and kinodynamic understanding to putt the ball.
Additionally, reflective reasoning is required if the feasibility of a
challenge is not ensured. We introduce RoboGolf, a VLM-based framework that
combines dual-camera perception with closed-loop action refinement, augmented
by a reflective equilibrium loop. The core of both loops is powered by
finetuned VLMs. We analyze the capabilities of the framework in an offline
inference setting, relying on an extensive set of recorded trajectories.
Exemplary demonstrations of the analyzed problem domain are available at
https://jity16.github.io/RoboGolf/
