RoboGolf: Mastering Real-World Minigolf with a Reflective Multi-Modality Vision-Language Model
<|reference_start|>RoboGolf: Mastering Real-World Minigolf with a Reflective Multi-Modality Vision-Language Model: 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/<|reference_end|>
@article{zhou2024robogolf:,
    title={RoboGolf: Mastering Real-World Minigolf with a Reflective Multi-Modality
  Vision-Language Model},
    author={Hantao Zhou, Tianying Ji, Lukas Sommerhalder, Michael Goerner, Norman
  Hendrich, Jianwei Zhang, Fuchun Sun, Huazhe Xu},
    journal={arXiv preprint arXiv:2406.10157},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10157},
    primaryClass={cs.RO cs.AI}
}