Fuente: WIPO "tomato" A method for long-horizon task planning in an embodied agent environment parses a current observation using a scene parser to generate a scene graph processed by a Graph Neural Network embedder to produce a current scene embedding vector. A lookahead simulator simulates execution of valid actions using a Planning Domain Definition Language-based forward dynamics model to produce action-observation pairs provided to a Large Language Model agent. An experience retriever compares the current scene embedding vector against an experience memory bank and, upon finding a match within a predefined threshold, retrieves a relevant experience comprising a goal, a textual description of a matched state, and a ground-truth action sequence. A loop detector identifies previously visited states and generates a loop warning. The Large Language Model agent processes a structured prompt comprising these components to select and execute an action, while a state-transition graph builder maintains a memory graph of state transitions.