用张量运算实现批量高效运动规划，支持平滑路径生成。
Global Tensor Motion Planning
- 基于随机多部图结构的张量化采样与碰撞检测
- 在激光扫描地图上批量规划速度比基线快数倍
- 无需梯度优化即可直接生成平滑轨迹，适合大规模机器人学习
批量运动规划在下游学习任务（如知识蒸馏和模仿学习）中愈发重要。本文提出全局张量运动规划（GTMP），一种仅由张量操作构成的采样式运动规划算法。我们引入一种新型离散化结构——随机多部图，实现高效的向量化采样、碰撞检测与搜索。理论分析表明，GTMP具备概率完备性，并兼容现代GPU/TPU。通过在多部图中融入光滑结构，GTMP可直接规划光滑样条，无需梯度优化。在激光雷达扫描的占据地图和MotionBenchMarker数据集上的实验显示，相比基线方法，GTMP在批量规划中具有显著计算效率优势，展现出作为鲁棒、可扩展规划器在多样化应用与大规模机器人学习任务中的潜力。
原文摘要 · Abstract (English)
Batch planning is increasingly necessary to quickly produce diverse and quality motion plans for downstream learning applications, such as distillation and imitation learning. This paper presents Global Tensor Motion Planning (GTMP) -- a sampling-based motion planning algorithm comprising only tensor operations. We introduce a novel discretization structure represented as a random multipartite graph, enabling efficient vectorized sampling, collision checking, and search. We provide a theoretical investigation showing that GTMP exhibits probabilistic completeness while supporting modern GPU/TPU. Additionally, by incorporating smooth structures into the multipartite graph, GTMP directly plans smooth splines without requiring gradient-based optimization. Experiments on lidar-scanned occupancy maps and the MotionBenchMarker dataset demonstrate GTMP's computation efficiency in batch planning compared to baselines, underscoring GTMP's potential as a robust, scalable planner for diverse applications and large-scale robot learning tasks.
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