Extracting commonsense knowledge from a large language model (LLM) offers a path to designing intelligent robots. Existing approaches that leverage LLMs for planning are unable to recover when an action fails and often resort to retrying failed actions, without resolving the error's underlying cause. We propose a novel approach (CAPE) that attempts to propose corrective actions to resolve precondition errors during planning. CAPE improves the quality of generated plans by leveraging few-shot reasoning from action preconditions. Our approach enables embodied agents to execute more tasks than baseline methods while ensuring semantic correctness and minimizing re-prompting. In VirtualHome, CAPE generates executable plans while improving a human-annotated plan correctness metric from 28.89% to 49.63% over SayCan. Our improvements transfer to a Boston Dynamics Spot robot initialized with a set of skills (specified in language) and associated preconditions, where CAPE improves the correctness metric of the executed task plans by 76.49% compared to SayCan. Our approach enables the robot to follow natural language commands and robustly recover from failures, which baseline approaches largely cannot resolve or address inefficiently.

翻译：摘要：从大语言模型中提取常识知识为设计智能机器人提供了一条路径。现有利用LLM进行规划的方法在动作失败时无法恢复，且往往仅重复失败的动作而不解决错误的根本原因。我们提出了一种新方法（CAPE），尝试在规划过程中生成纠正动作以解决前提错误。CAPE通过利用动作前提的少样本推理提高了生成规划的质量。我们的方法使具身代理能够比基线方法执行更多任务，同时确保语义正确性并最小化重新提示。在VirtualHome中，CAPE生成了可执行的规划，并将人工标注的规划正确性指标从SayCan的28.89%提升至49.63%。我们的改进可迁移至配备一组语言描述技能及其相关前提的波士顿动力Spot机器人，此时CAPE将执行任务规划的正确性指标较SayCan提升了76.49%。我们的方法使机器人能够遵循自然语言指令并稳健地从失败中恢复，而基线方法大多无法解决或处理低效此类问题。