指令增强的长时程规划：在具身移动操作中嵌入接地机制
Instruction-Augmented Long-Horizon Planning: Embedding Grounding Mechanisms in Embodied Mobile Manipulation
AI总结：
本文提出IALP系统，将LLM抽象推理与接地机制结合，把用户指令增强为PDDL问题，在真实长时程移动操作任务中实现超过80%的平均成功率。
摘要：
使人形机器人能够基于具身感知与理解能力在真实环境中执行长时程移动操作规划，一直是一个长期存在的挑战。随着大语言模型（LLMs）近年来的兴起，基于LLM的规划器开发显著增加。这些方法要么利用人类提供的真实世界文本表示，要么严重依赖提示工程来提取此类表示，缺乏定量理解环境的能力，例如判断操作物体的可行性。为了解决这些局限性，我们提出了指令增强长时程规划（IALP）系统，这是一个新颖的框架，在闭环交互中利用LLM基于实时传感器反馈（包括环境的接地知识）生成可行且最优的动作。与先前工作不同，我们的方法通过同时利用LLM的抽象推理能力和接地机制，将用户指令增强为PDDL问题。通过执行各种真实世界长时程任务（每个任务由七种不同的操作技能组成），我们的结果表明IALP系统能够高效解决这些任务，平均成功率超过80%。我们提出的方法可以作为高层规划器运行，通过利用多模态传感器输入，使机器人在非结构化环境中具备显著的自主性。
英文摘要：
Enabling humanoid robots to perform long-horizon mobile manipulation planning in real-world environments based on embodied perception and comprehension abilities has been a longstanding challenge. With the recent rise of large language models (LLMs), there has been a notable increase in the development of LLM-based planners. These approaches either utilize human-provided textual representations of the real world or heavily depend on prompt engineering to extract such representations, lacking the capability to quantitatively understand the environment, such as determining the feasibility of manipulating objects. To address these limitations, we present the Instruction-Augmented Long-Horizon Planning (IALP) system, a novel framework that employs LLMs to generate feasible and optimal actions based on real-time sensor feedback, including grounded knowledge of the environment, in a closed-loop interaction. Distinct from prior works, our approach augments user instructions into PDDL problems by leveraging both the abstract reasoning capabilities of LLMs and grounding mechanisms. By conducting various real-world long-horizon tasks, each consisting of seven distinct manipulatory skills, our results demonstrate that the IALP system can efficiently solve these tasks with an average success rate exceeding 80%. Our proposed method can operate as a high-level planner, equipping robots with substantial autonomy in unstructured environments through the utilization of multi-modal sensor inputs.
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。
