# 用于刚体运动与操纵规划的 Shape-Aware Neural Time Fields

> 原文链接：https://robotics.hellobike.com/papers/2026-10-16-2610-22762
> 频道：papers | 更新日期：2026-10-16 | 标签：arxiv, cs.RO
> 核心摘要：针对杂乱环境中位姿耦合的刚体运动规划，提出 Shape-Aware Neural Time Fields，并在2D/3D规划和推物操纵中验证。

# 用于刚体运动与操纵规划的 Shape-Aware Neural Time Fields

::: v-pre
> 原文链接：[Shape-Aware Neural Time Fields for Rigid-Body Motion and Manipulation Planning](https://arxiv.org/abs/2610.22762)（arXiv 2610.22762 · 发布日期：2026-10-16）

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

针对杂乱环境中位姿耦合的刚体运动规划，提出 Shape-Aware Neural Time Fields，并在2D/3D规划和推物操纵中验证。

## 核心要点

- 刚体运动规划中，平移与旋转受周围几何约束不同，使位姿耦合规划更具挑战。
- 方法学习可复用的旅行时间/到达代价表示，显式建模平移和旋转的几何限制。
- 该场与采样式规划器结合，在朝目标推进和到达邻近位姿代价之间进行平衡。
- 实验显示相较神经规划基线提升成功率和推理效率，并以更低时间接近经典采样式规划器。

**验证范围与局限**：验证范围包括2D和3D刚体规划任务、多样环境中的推物非抓取操纵，以及存在执行偏差和外部扰动的闭环推物实验。

## 英文摘要 (Abstract)

Rigid-body motion planning is fundamental to robotic transport and manipulation, where both object position and orientation determine whether a body can move through cluttered environments. This coupling makes planning chal- lenging because translation and rotation can be constrained very differently by surrounding geometry. We propose Shape- Aware Neural Time Fields, a physics-informed neural motion planning framework that learns a reusable travel-time, or cost- to-go, representation for rigid-body motion while explicitly accounting for these distinct geometric restrictions. The learned field captures local motion difficulty in translation and rotation and is paired with a sampling-based planner that balances progress toward the goal with the cost of reaching nearby poses. We evaluate the framework across 2D and 3D rigid- body planning tasks and non-prehensile manipulation through object pushing in diverse environments. Results show substantial improvements in planning success and inference efficiency over physics-informed neural planning baselines, while remaining competitive with classical sampling-based planners at significantly lower planning times. Ablation studies further validate the contributions of both the learned representation and the proposed planning strategy. Finally, closed-loop pushing experiments under execution deviations and external disturbances demonstrate that fast replanning enables reactive and adaptive task execution.

作者：Yanming Zhou, Yuchen Liu, Ahmed H. Qureshi
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## 原始参考来源 (Sources)

- [arXiv 2610.22762：Shape-Aware Neural Time Fields for Rigid-Body Motion and Manipulation Planning](https://arxiv.org/abs/2610.22762)