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arXiv AI By Haotian Zhai, Tianming Sha, Junnan Li

AtomTree: A Hierarchical Framework for State-Aware Embodied Instruction Following with LLMs

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AtomTree is a hierarchical framework that enables state-aware task planning for embodied instruction following using large language models (LLMs). It incrementally constructs a tree of subgoals and actions, allowing the LLM to decide at each step whether to decompose further or output a grounded low-level action based on the current symbolic state. The system includes a lightweight memory module to summarize past outcomes and employs hierarchical backtracking replanning when a low-level action stalls, thereby preventing drift along incorrect trajectories. In zero-shot evaluation on the ALFRED benchmark, AtomTree outperforms prior LLM-based planners.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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