Abstract
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.
Abstract (translated)
URL
https://arxiv.org/abs/2609.39153
