从语言到行动：基于大语言模型的代理能否用于具身机器人认知？
From Language to Action: Can LLM-Based Agents Be Used for Embodied Robot Cognition?
AI总结：
本文研究了基于大语言模型的代理在家庭任务中的表现，探讨其在具身机器人认知中的潜力与挑战。
摘要：
为了在日常环境中灵活行动，机器人代理需要多种认知能力，使它能够对计划进行推理并执行恢复。大语言模型（LLMs）已被证明能够展示出涌现的认知方面，如推理和语言理解；然而，控制具身机器人代理的能力需要可靠地将高层语言连接到低层功能，以实现感知和控制。在本文中，我们探讨LLM在认知机器人架构中作为核心组件进行规划和执行推理的程度。为此，我们提出了一种认知架构，其中一个代理LLM作为规划和推理的核心组件，而用于工作和事件记忆的组件支持从经验中学习和适应。然后，该架构的一个实例被用于控制模拟家庭环境中的移动机械臂，其中环境交互通过一组高层工具进行，包括感知、推理、导航、抓取和放置，所有这些工具都向基于LLM的代理提供。我们评估了所提出的系统在两个家庭任务（物体放置和物体交换）上的表现，这些任务评估了代理的推理、规划和记忆利用。结果表明，LLM驱动的代理能够完成结构化任务，并表现出涌现的适应性和记忆引导的规划，但也揭示了显著的局限性，如对任务成功的幻觉和对指令的执行不佳，例如拒绝承认并完成连续任务。这些发现突显了在自主机器人中使用LLM作为具身认知控制器的潜力和挑战。
英文摘要：
In order to flexibly act in an everyday environment, a robotic agent needs a variety of cognitive capabilities that enable it to reason about plans and perform execution recovery. Large language models (LLMs) have been shown to demonstrate emergent cognitive aspects, such as reasoning and language understanding; however, the ability to control embodied robotic agents requires reliably bridging high-level language to low-level functionalities for perception and control. In this paper, we investigate the extent to which an LLM can serve as a core component for planning and execution reasoning in a cognitive robot architecture. For this purpose, we propose a cognitive architecture in which an agentic LLM serves as the core component for planning and reasoning, while components for working and episodic memories support learning from experience and adaptation. An instance of the architecture is then used to control a mobile manipulator in a simulated household environment, where environment interaction is done through a set of high-level tools for perception, reasoning, navigation, grasping, and placement, all of which are made available to the LLM-based agent. We evaluate our proposed system on two household tasks (object placement and object swapping), which evaluate the agent's reasoning, planning, and memory utilisation. The results demonstrate that the LLM-driven agent can complete structured tasks and exhibits emergent adaptation and memory-guided planning, but also reveal significant limitations, such as hallucinations about the task success and poor instruction following by refusing to acknowledge and complete sequential tasks. These findings highlight both the potential and challenges of employing LLMs as embodied cognitive controllers for autonomous robots.
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。
