苏格拉底规划器：基于自问答的具身指令跟随零样本规划
Socratic Planner: Self-QA-Based Zero-Shot Planning for Embodied Instruction Following
AI总结：
本文提出苏格拉底规划器，一种基于自问答的零样本规划方法，通过LLM自我问答生成子目标序列，并利用视觉反馈重新规划，在ALFRED基准上超越现有最先进模型，尤其在长时程任务中表现优异，并已在实体机器人上验证。该方法无需训练即可实现具身指令跟随的组合任务规划。
摘要：
具身指令跟随（EIF）是这样一个任务：通过在交互式环境中导航并与物体交互来执行自然语言指令。EIF中的一个关键挑战是组合任务规划，通常通过监督学习或使用标注数据的少样本上下文学习来解决。为此，我们引入了苏格拉底规划器（Socratic Planner），一种基于自问答的零样本规划方法，无需进一步训练即可推断出合适的计划。苏格拉底规划器首先促进大语言模型（LLM）进行自我提问和回答，这反过来有助于生成一系列子目标。在执行子目标时，具身智能体可能会遇到意外情况，例如未预见的障碍。然后，苏格拉底规划器通过基于视觉的重新规划机制，根据密集的视觉反馈调整计划。实验证明了苏格拉底规划器的有效性，在ALFRED基准上，它在所有指标上均优于当前最先进的规划模型，尤其是在需要复杂推理的长时程任务中表现出色。我们进一步通过在实体机器人上部署以执行长时程任务，展示了其实际应用价值。
英文摘要：
Embodied Instruction Following (EIF) is the task of executing natural language instructions by navigating and interacting with objects in interactive environments. A key challenge in EIF is compositional task planning, typically addressed through supervised learning or few-shot in-context learning with labeled data. To this end, we introduce the Socratic Planner, a self-QA-based zero-shot planning method that infers an appropriate plan without any further training. The Socratic Planner first facilitates self-questioning and answering by the Large Language Model (LLM), which in turn helps generate a sequence of subgoals. While executing the subgoals, an embodied agent may encounter unexpected situations, such as unforeseen obstacles. The Socratic Planner then adjusts plans based on dense visual feedback through a visually-grounded re-planning mechanism. Experiments demonstrate the effectiveness of the Socratic Planner, outperforming current state-of-the-art planning models on the ALFRED benchmark across all metrics, particularly excelling in long-horizon tasks that demand complex inference. We further demonstrate its real-world applicability through deployment on a physical robot for long-horizon tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
