AI 中文总结
提出一种自适应高层规划方法，交错进行任务驱动的语义搜索与LTL任务执行，利用VLM构建的场景图和DFA选择关键路点，在未知环境中以更短路径完成更多任务。
AI 中文摘要
在未知环境中规划复杂任务，要求机器人同时推理它们应该做什么以及还需要发现什么。现有的解决LTLf任务的方法通常假设环境已知，或将环境的探索与任务的执行分开，而语义探索方法一次只寻找一个目标，并忽略正在执行的任务。为了填补这一空白，我们的主要贡献是一种自适应的高层规划方法，该方法将任务驱动的语义搜索与任务的执行交错进行，以非短视的方式推进两者。我们的方法利用在线构建的两种表示：一种是由视觉语言模型（VLM）构建的度量-语义场景图，它提供了定位任务所指对象所需的证据；另一种是编码任务的确定性有限自动机（DFA），它指示在每个任务状态下哪些对象是重要的。在每个规划阶段，我们的规划器选择对语义搜索和任务推进都最有价值的路点，并在剩余任务阶段上对其进行评估，以避免阻塞状态。然后，所选路点被排序成一个单一的高层计划，该计划随着新信息的到来而重新计算。在五种任务类型的逼真室内环境中，我们的方法比对比方法完成了更多的任务，同时需要覆盖更少的环境，并且以更短的路径完成，同时遵守任务施加的限制。
英文摘要
Planning complex missions in unknown environments requires robots to reason simultaneously about what they should do and what they still need to discover. Existing approaches for solving LTLf missions typically assume a known environment, or separate the exploration of the environment from the execution of the mission, while semantic exploration methods look for one target at a time and ignore the mission being executed. To fill this gap, our main contribution is an adaptive high-level planning method that interleaves a task-driven semantic search with the execution of the mission, advancing both in a non-myopic manner. Our method leverages two representations built online, a metric-semantic scene graph, built with a Vision Language Model (VLM), that provides the evidence needed to locate the objects the mission refers to, and the deterministic finite automaton (DFA) encoding the mission, that indicates which of them matter at each mission state. At every planning stage, our planner selects the waypoints that are most valuable for both the semantic search and the advancement of the mission, valuing them over the remaining mission stages in order to avoid blocking states. The selected waypoints are then ordered in a single high-level plan, which is recomputed as new information arrives. In photorealistic indoor environments over five mission types, our method completes more missions than the compared approaches while having to cover less of the environment, and it does so with shorter paths and complying with the restrictions imposed by the mission.
Comments8 pages, 4 figures
