@inproceedings{wang-etal-2024-e2cl,
    title = "{E}$^2${CL}: Exploration-based Error Correction Learning for Embodied Agents",
    author = "Wang, Hanlin  and
      Leong, Chak Tou  and
      Wang, Jian  and
      Li, Wenjie",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.findings-emnlp.448/",
    doi = "10.18653/v1/2024.findings-emnlp.448",
    pages = "7626--7639",
    abstract = "Language models are exhibiting increasing capability in knowledge utilization and reasoning. However, when applied as agents in embodied environments, they often suffer from misalignment between their intrinsic knowledge and environmental knowledge, leading to infeasible actions. Traditional environment alignment methods, such as supervised learning on expert trajectories and reinforcement learning, encounter limitations in covering environmental knowledge and achieving efficient convergence, respectively. Inspired by human learning, we propose Exploration-based Error Correction Learning (E$^2$CL), a novel framework that leverages exploration-induced errors and environmental feedback to enhance environment alignment for embodied agents. E$^2$CL incorporates teacher-guided and teacher-free explorations to gather environmental feedback and correct erroneous actions. The agent learns to provide feedback and self-correct, thereby enhancing its adaptability to target environments. Extensive experiments in the VirtualHome environment demonstrate that E$^2$CL-trained agents outperform those trained by baseline methods and exhibit superior self-correction capabilities."
}