AutoPath：学习可转移的目标条件随机路径先验以实现无人类示范的安全导航
AutoPath: Learning Transferable Goal-Conditioned Stochastic Path Prior for Safe Navigation Without Human Demonstrations
- Zhejiang University（浙江大学）
- Harbin Institute of Technology（哈尔滨工业大学）
- Ant Group（蚂蚁集团）
- Shanghai Jiao Tong University（上海交通大学）
- Shenzhen University（深圳大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究在复杂环境下的安全导航问题，提出学习可转移目标条件随机路径先验的方法，引入规范状态表示和结构化先验学习框架，实验证明该方法成功率高、效率有竞争力且可跨平台转移。
AI中文摘要：
在杂乱和动态环境中的实时导航需要在有限感知下实现无碰撞且动态可行的运动。然而，可行的导航行为本质上是多模态的，因为障碍物周围可能存在多条路径。本文将导航表述为学习一种可转移的目标条件随机路径先验，它基于局部观测对与目标对齐且几何一致的局部路径建模可重复使用的分布。为此引入目标对齐的规范状态表示，开发结构化先验学习框架。实验表明该方法成功率高、效率有竞争力，且能跨平台转移。
英文摘要：
Real-time navigation in cluttered and dynamic environments requires collision-free and dynamically feasible motion under limited perception. However, feasible navigation behaviors are inherently multimodal because multiple paths may exist around obstacles. In this paper, we formulate navigation as learning a transferable goal-conditioned stochastic path prior that models a reusable distribution over goal-aligned geometry-consistent local paths conditioned on local observations. This formulation enables structured sampling of navigation candidates, allowing multiple feasible paths to be explored through sampling without relying on robot-specific motion constraints. To this end, we introduce a goal-aligned canonical state representation that removes in-plane rotational ambiguity and normalizes local geometry with respect to the goal, enabling rotation-invariant path distribution learning. We further develop a structured prior learning framework that parameterizes local paths using a geometry-aware polar action manifold and incorporates risk-sensitive utility shaping with multi-goal distributional rollouts for stable and safety-aware planning. Extensive experiments in dense static environments and dynamic pedestrian scenarios demonstrate that the proposed method achieves consistently high success rates with competitive efficiency while enabling cross-platform transfer of a single path prior learned on differential-drive robots to quadruped platforms without retraining.
