用稀疏路网弱监督，提升复杂环境下的物理感知运动规划鲁棒性
Weakly-supervised Learning for Physics-informed Neural Motion Planning via Sparse Roadmap
- 结合稀疏路网与偏微分方程约束，实现弱监督下的分层规划
- 在18个Gibson环境中显著降低局部最优陷阱，提升全局一致性
- 适合需快速推理的机器人路径规划场景，尤其复杂多室环境
运动规划需在高维、杂乱空间中找到起点到目标的无碰撞路径。现有基于学习的方法虽有进展，但物理信息驱动方法如神经时间场（NTFields）在复杂多室环境中仍难扩展，单纯增加采样无法克服局部极小或保证全局一致性。本文提出分层神经时间场（H-NTFields），结合稀疏路网提供的上下界时间约束与物理信息偏微分方程正则化。路网提供全局拓扑锚点，PDE损失确保局部几何保真与避障传播。在18个Gibson环境及真实机器人平台上的实验表明，该方法显著提升鲁棒性，同时通过连续值表示实现快速摊销推理。
原文摘要 · Abstract (English)
The motion planning problem requires finding a collision-free path between start and goal configurations in high-dimensional, cluttered spaces. Recent learning-based methods offer promising solutions, with self-supervised physics-informed approaches such as Neural Time Fields (NTFields) solving the Eikonal equation to learn value functions without expert demonstrations. However, existing physics-informed methods struggle to scale in complex, multi-room environments, where simply increasing the number of samples cannot resolve local minima or guarantee global consistency. We propose Hierarchical Neural Time Fields (H-NTFields), a weakly-supervised framework that combines weak supervision from sparse roadmaps with physics-informed PDE regularization. The roadmap provides global topological anchors through upper and lower bounds on travel times, while PDE losses enforce local geometric fidelity and obstacle-aware propagation. Experiments on 18 Gibson environments and real robotic platforms show that H-NTFields substantially improves robustness over prior physics-informed methods, while enabling fast amortized inference through a continuous value representation.
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