RoboGPT: an intelligent agent of making embodied long-term decisions for
  daily instruction tasks
Yaran Chen Wenbo Cui Yuanwen Chen Mining Tan Xinyao Zhang Dongbin Zhao He Wang
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.