Enabling robotic agents to perform complex long-horizon tasks has been a long-standing goal in robotics and artificial intelligence (AI). Despite the potential shown by large language models (LLMs), their planning capabilities remain limited to short-horizon tasks and they are unable to replace the symbolic planning approach. Symbolic planners, on the other hand, may encounter execution errors due to their common assumption of complete domain knowledge which is hard to manually prepare for an open-world setting. In this paper, we introduce a Language-Augmented Symbolic Planner (LASP) that integrates pre-trained LLMs to enable conventional symbolic planners to operate in an open-world environment where only incomplete knowledge of action preconditions, objects, and properties is initially available. In case of execution errors, LASP can utilize the LLM to diagnose the cause of the error based on the observation and interact with the environment to incrementally build up its knowledge base necessary for accomplishing the given tasks. Experiments demonstrate that LASP is proficient in solving planning problems in the open-world setting, performing well even in situations where there are multiple gaps in the knowledge.

翻译：使机器人代理能够执行复杂的长期任务一直是机器人与人工智能领域的长期目标。尽管大型语言模型展现出潜力，但其规划能力仍局限于短期任务，无法替代符号规划方法。另一方面，符号规划器因其通常假设具备完整的领域知识——这在开放世界场景中难以手动准备——而可能遭遇执行错误。本文提出一种语言增强符号规划器，它通过集成预训练的大型语言模型，使传统符号规划器能够在仅具备不完整动作前提条件、对象及属性初始知识的开放世界环境中运行。当出现执行错误时，该规划器可利用大型语言模型基于观测结果诊断错误原因，并通过与环境交互逐步构建完成给定任务所需的知识库。实验表明，该规划器能够熟练解决开放世界环境中的规划问题，即使在知识存在多重缺失的情况下仍表现良好。