花婆の世界 : チワン族における花婆信仰習俗について [全文の要約]

関西大

花婆の世界 : チワン族における花婆信仰習俗について [論文要旨及び審査の要旨]

関西大

The custom in the belief of Yah va among the Zhuang people

In the Zhuang people\u27 custom, the goddess Yah va was commonly inshrined at their own altas. In addition, the Zhuang people would preside over religious ceremonies that related to goddess Yah va when significant moments, like various life ritual (birthday, adult ceremony, wedding, funeral) or any other big events such as being sick, moving somewhere to live, or having no babies et al, came to their lives. A comprehensive analysis and summary of fork activities of the Yah va through documental materials and field researches.文部科学省グローバルCOEプログラム 関西大学文化交渉学教育研究拠点[東アジアの思想と構造

RoboGPT: an intelligent agent of making embodied long-term decisions for daily instruction tasks

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