用生成模型分步规划，结合时间信息选最优子目标。
Planning with Learned Subgoals Selected by Temporal Information
- 通过生成模型逐步构建可执行的子目标
- 利用时间估计器筛选满足时序约束的子目标
- 适合动态环境中需兼顾时间和路径的机器人规划
在动态环境中进行路径规划极具挑战性，因为移动物体带来了依赖时间的约束。现有规划方法主要关注空间因素，难以直接融入时间约束。本文提出一种新方法：利用生成模型根据当前规划上下文逐步生成子目标，将复杂问题分解为多个小任务；随后结合时间信息，基于不同统计分布的时序估计器对生成的子目标候选进行评估与选择。实验表明，从当前机器人状态出发，规划至所选子目标，能够满足给定的时间依赖约束，同时保持目标导向性。
原文摘要 · Abstract (English)
Path planning in a changing environment is a challenging task in robotics, as moving objects impose time-dependent constraints. Recent planning methods primarily focus on the spatial aspects, lacking the capability to directly incorporate time constraints. In this paper, we propose a method that leverages a generative model to decompose a complex planning problem into small manageable ones by incrementally generating subgoals given the current planning context. Then, we take into account the temporal information and use learned time estimators based on different statistic distributions to examine and select the generated subgoal candidates. Experiments show that planning from the current robot state to the selected subgoal can satisfy the given time-dependent constraints while being goal-oriented.
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