Robotic agents must master common sense and long-term sequential decisions to solve daily tasks through natural language instruction. The developments in Large Language Models (LLMs) in natural language processing have inspired efforts to use LLMs in complex robot planning. Despite LLMs' great generalization and comprehension of instruction tasks, LLMs-generated task plans sometimes lack feasibility and correctness. To address the problem, we propose a RoboGPT agent\footnote{our code and dataset will be released soon} for making embodied long-term decisions for daily tasks, with two modules: 1) LLMs-based planning with re-plan to break the task into multiple sub-goals; 2) RoboSkill individually designed for sub-goals to learn better navigation and manipulation skills. The LLMs-based planning is enhanced with a new robotic dataset and re-plan, called RoboGPT. The new robotic dataset of 67k daily instruction tasks is gathered for fine-tuning the Llama model and obtaining RoboGPT. RoboGPT planner with strong generalization can plan hundreds of daily instruction tasks. Additionally, a low-computational Re-Plan module is designed to allow plans to flexibly adapt to the environment, thereby addressing the nomenclature diversity challenge. The proposed RoboGPT agent outperforms SOTA methods on the ALFRED daily tasks. Moreover, RoboGPT planner exceeds SOTA LLM-based planners like ChatGPT in task-planning rationality for hundreds of unseen daily tasks, and even other domain tasks, while keeping the large model's original broad application and generality.

翻译：机器人智能体必须掌握常识与长期序列决策能力，以通过自然语言指令解决日常任务。自然语言处理领域大语言模型（LLMs）的进展激发了人们将LLMs用于复杂机器人规划的研究。尽管LLMs在指令任务中表现出强大的泛化与理解能力，但其生成的任务计划常缺乏可行性与正确性。为解决该问题，我们提出RoboGPT智能体（代码与数据集即将开源），用于实现日常任务的具身长期决策，包含两个模块：1）基于LLMs并支持重规划的规划模块，将任务分解为多个子目标；2）为子目标单独设计的RoboSkill模块，用于学习更优的导航与操作技能。该基于LLMs的规划方法通过新型机器人数据集与重规划增强，称为RoboGPT。我们收集了包含6.7万条日常指令任务的新型机器人数据集，用于微调Llama模型并获得RoboGPT。具有强泛化能力的RoboGPT规划器可规划数百种日常指令任务。此外，我们设计了低算力消耗的重规划模块，使规划能灵活适应环境，从而解决命名多样性挑战。所提RoboGPT智能体在ALFRED日常任务中优于SOTA方法。此外，RoboGPT规划器在数百种未见日常任务甚至其他领域任务中的任务规划合理性上，超越ChatGPT等基于LLMs的SOTA规划器，同时保持大模型的广泛适用性与通用性。