Masked Generative Motion Planning with Geometry-Guided Token Search

This research proposes MGMP, a generative motion planning framework that uses masked transformers and geometry-guided token search to enable efficient route-level restructuring. It achieves 96% success on Ring Maze and 25% higher repair success than baselines on Kuka, with generalization to new layouts, obstacles, and real-world tasks.