Large-Language-Model-Guided State Estimation for Partially Observable Task and Motion Planning

ArXi:2603.03704v2 Announce Type: replace-cross Robot planning in partially observable environments, where not all objects are known or visible, is a challenging problem, as it requires reasoning under uncertainty through partially observable Marko decision processes. During the execution of a computed plan, a robot may unexpectedly observe task-irrelevant objects, which are typically ignored by naive planners.