@inproceedings{farag-etal-2025-conditional,
    title = "Conditional Multi-Stage Failure Recovery for Embodied Agents",
    author = "Farag, Youmna  and
      Stoyanchev, Svetlana  and
      Li, Mohan  and
      Keizer, Simon  and
      Doddipatla, Rama",
    editor = "Kamalloo, Ehsan  and
      Gontier, Nicolas  and
      Lu, Xing Han  and
      Dziri, Nouha  and
      Murty, Shikhar  and
      Lacoste, Alexandre",
    booktitle = "Proceedings of the 1st Workshop for Research on Agent Language Models (REALM 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.realm-1.15/",
    pages = "200--227",
    ISBN = "979-8-89176-264-0",
    abstract = "Embodied agents performing complex tasks are susceptible to execution failures, motivating the need for effective failure recovery mechanisms. In this work, we introduce a conditional multi-stage failure recovery framework that employs zero-shot chain prompting. The framework is structured into four error-handling stages, with three operating during task execution and one functioning as a post-execution reflection phase.Our approach utilises the reasoning capabilities of LLMs to analyse execution challenges within their environmental context and devise strategic solutions.We evaluate our method on the TfD benchmark of the TEACH dataset and achieve state-of-the-art performance, outperforming a baseline without error recovery by 11.5{\%} and surpassing the strongest existing model by 19{\%}."
}