Anytime Global Tensor Motion Planning

2026-08-26

AI Summary

本文通过使用任意黑盒局部规划器和两种随时策略改进了全局张量运动规划，解决了运动规划问题，并在不同基准测试中表现出色。

Abstract

Global Tensor Motion Planning (GTMP) solves motion planning with batched tensor operations over a layered multipartite graph. We generalize GTMP so that adjacent-layer edges are realized by any black-box local planner (e.g., linear interpolation, splines, sampling-based planning, trajectory optimization, or generative sampling). We provide two anytime policies on top of this generalization: Anytime GTMP with random restarts at a fixed budget, which covers every homotopy class almost surely, and AO-GTMP with informed expansion with growing budgets, which converges to the optimal cost. We prove that a single sampled graph covers every endpoint-fixed homotopy class admitting a \(δ\)-clear representative of bounded length. We also prove that additional samples per layer reduce the per-layer miss probability exponentially, whereas stronger local planners reduce the required layer count only sublinearly. On manipulation benchmarks the method matches state-of-the-art performance, and on 2D navigation it returns batches of topologically diverse solutions, while the informed baselines concentrate on one or two classes.

Problem

• Global Tensor Motion Planning
• black-box local planner
• anytime policies
• homotopy class
• optimal cost

Innovation

• Anytime Global Tensor Motion Planning
• black-box local planner
• homotopy class
• informed expansion
• topologically diverse solutions