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arXiv · 2605.25851

RePlan-Bot: Multi-Level Replanning for Embodied Instruction Following

Xicheng Gong·Guozheng Sun·Peiran Xu·Yadong Mu
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Abstract

Embodied instruction following (EIF) requires agents to understand and execute complex natural language commands within interactive 3D environments. Despite recent advances, existing methods often fail in long-horizon planning and handling irreversible state changes, resulting in low task success rates. To address these challenges, we introduce RePlan-Bot, a novel EIF agent that performs multi-level, continuous replanning throughout task execution. RePlan-Bot integrates a high-level LLM-based auditor for dynamic sub-goal adjustments guided by environmental feedback, a commonsense-guided search mechanism based on a multi-layered instance map for precise and structured object localization, and a lightweight ViT-based corrector to preemptively fix risky low-level actions. Evaluated on the ALFRED benchmark, RePlan-Bot achieves state-of-the-art performance in both seen and unseen environments, demonstrating superior adaptability and reliability.

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Xicheng Gong, Guozheng Sun, Peiran Xu, Yadong Mu. 2026-05-25. RePlan-Bot: Multi-Level Replanning for Embodied Instruction Following. https://arxiv.org/abs/2605.25851

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