AtomTree: A Hierarchical Framework for State-Aware Embodied Instruction Following with LLMs
arXiv:2610.38744v1 Announce Type: cross Abstract: Embodied instruction following in interactive environments requires agents to interpret natural language commands and complete long-horizon tasks. Existing planners either rely on large labeled trajectories, or use few-shot and zero-shot LLM prompting that produces a whole plan in a single serial reasoning process. These approaches depend on linguistic common sense, commit to a plan from the initial instruction, and only update after failures, w…