LLM+MAP：利用大型语言模型与规划域定义语言的双臂机器人任务规划
LLM+MAP: Bimanual Robot Task Planning using Large Language Models and Planning Domain Definition Language
AI总结：
本文提出LLM+MAP框架，结合LLM推理与基于PDDL的多智能体规划，解决双臂机器人长程任务的分解与分配问题，并在仿真中验证其优于多种直接使用LLM的规划方法。
摘要：
双臂机器人操作提供了显著的多功能性，但由于涉及双手之间空间与时间协调的复杂性，也带来了内在挑战。现有工作主要聚焦于让机器人手获得人类水平的操作技能，却很少关注长时间尺度上的任务规划。凭借出色的上下文学习和零样本生成能力，大型语言模型（LLM）已被应用并落地到多种机器人实体中，以促进任务规划。然而，LLM在复杂机器人任务中仍存在长程推理错误和幻觉问题，在生成规划时缺乏逻辑正确性保证。此前的工作（如LLM+P）使用符号规划器扩展LLM。但尚无工作成功应用于双臂机器人。双臂操作不可避免地出现新挑战，不仅需要有效的任务分解，还需要高效的任务分配。为应对这些挑战，本文提出LLM+MAP，这是一个集成LLM推理与多智能体规划的双臂规划框架，可自动化实现有效且高效的双臂任务规划。我们在不同复杂度的多种长程操作任务上开展仿真实验。该方法以GPT-4o作为后端构建，并将其性能与LLM直接生成的规划进行比较，包括GPT-4o、V3以及近期强大的推理模型o1和R1。通过分析规划时间、成功率、组代价和规划步骤减少率等指标，我们证明了LLM+MAP的优越性能，同时也提供了对机器人推理的洞见。代码可在https://github.com/Kchu/LLM-MAP获取。
英文摘要：
Bimanual robotic manipulation provides significant versatility, but also presents an inherent challenge due to the complexity involved in the spatial and temporal coordination between two hands. Existing works predominantly focus on attaining human-level manipulation skills for robotic hands, yet little attention has been paid to task planning on long-horizon timescales. With their outstanding in-context learning and zero-shot generation abilities, Large Language Models (LLMs) have been applied and grounded in diverse robotic embodiments to facilitate task planning. However, LLMs still suffer from errors in long-horizon reasoning and from hallucinations in complex robotic tasks, lacking a guarantee of logical correctness when generating the plan. Previous works, such as LLM+P, extended LLMs with symbolic planners. However, none have been successfully applied to bimanual robots. New challenges inevitably arise in bimanual manipulation, necessitating not only effective task decomposition but also efficient task allocation. To address these challenges, this paper introduces LLM+MAP, a bimanual planning framework that integrates LLM reasoning and multi-agent planning, automating effective and efficient bimanual task planning. We conduct simulated experiments on various long-horizon manipulation tasks of differing complexity. Our method is built using GPT-4o as the backend, and we compare its performance against plans generated directly by LLMs, including GPT-4o, V3 and also recent strong reasoning models o1 and R1. By analyzing metrics such as planning time, success rate, group debits, and planning-step reduction rate, we demonstrate the superior performance of LLM+MAP, while also providing insights into robotic reasoning. Code is available at https://github.com/Kchu/LLM-MAP.
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