arXiv Machine Learning October 19, 2026 By Yanming Zhou, Yuchen Liu, Ahmed H. Qureshi

Shape-Aware Neural Time Fields for Rigid-Body Motion and Manipulation Planning

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Shape-Aware Neural Time Fields is a physics‑informed neural motion planning framework that learns a reusable travel‑time (cost‑to‑go) representation for rigid‑body motion, explicitly accounting for distinct geometric restrictions on translation and rotation. The learned field captures local motion difficulty and is combined with a sampling‑based planner that balances progress toward the goal with the cost of reaching nearby poses. Experiments on 2D and 3D planning tasks and non‑prehensile manipulation show substantial improvements in planning success and inference efficiency over neural baselines, while remaining competitive with classical planners at lower planning times, and closed‑loop pushing tests demonstrate fast replanning for reactive execution.

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