发表机构
University of Glasgow（格拉斯哥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出MGMP算法，结合GGTS技术，在运动规划任务中大幅提升成功率，且具备良好泛化能力，优于现有基准方法。
AI 中文摘要
生成式运动规划器通常使用学习到的轨迹先验进行初始生成，而将测试时的修复留给局部连续优化。我们提出掩码生成运动规划（MGMP），它将学习到的先验从高效并行生成扩展到结构修复。掩码生成变换器并行生成离散轨迹候选，几何引导令牌搜索（GGTS）利用场景几何定位编辑位置并评估先验支持的替代方案，这将修复转化为对离散运动替代方案的高效搜索，实现超越局部轨迹变形的路线级重构。MGMP在环形迷宫上达到96%的成功率，在Kuka机械臂的受控路线失效任务上达到82%的修复成功率，分别比最强的外部基准高出23和25个百分点。它还能泛化到未见布局、额外障碍物、未见几何、单臂和双臂规划，以及现实世界的Baxter任务。
英文摘要
Generative motion planners typically use learned trajectory priors for initial generation, while leaving test-time repair to local continuous refinement. We introduce Masked Generative Motion Planning (MGMP), which extends the learned prior from efficient parallel generation to structural repair. A masked generative transformer generates discrete trajectory candidates in parallel, and Geometry-Guided Token Search (GGTS) uses scene geometry to target where to edit and which prior-supported alternatives to evaluate. This turns refinement into an efficient search over discrete motion alternatives, enabling route-level restructuring beyond local trajectory deformation. MGMP achieves 96% success on Ring Maze and 82% repair success on Controlled Route Invalidation on Kuka, exceeding the strongest external baselines by 23 and 25 percentage points, respectively. It further generalizes to unseen layouts, additional obstacles, unseen geometries, single- and dual-arm planning, and real-world Baxter tasks.
