Anytime Global Tensor Motion Planning

• URL: http://arxiv.org/abs/2608.25830v1
• Date: Wed, 26 Aug 2026 14:08:22 GMT
• Title: Anytime Global Tensor Motion Planning
• Abstract summary: Global Motion Planning (GTMP) solves motion planning with batched tensor operations over a layered multipartite graph.<n>We generalize GTMP so that adjacent-layer edges are realized by any black-box local planner.<n>We provide two anytime policies on top of this generalization: Anytime GTMP with random restarts at a fixed budget, and AO-GTMP with informed expansion with growing budgets.
• Score: 1.1470070927586018
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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