使用Pepper探索多式联动讲故事:一项初步研究,使用零射击的LLM
Unai Zabala1, Juan Echevarria1, Igor Rodriguez1
1Department of Computer Science and Artificial Intelligence, University of the Basque Country (EHU), Donostia, Spain.
Frontiers in robotics and AI
|October 24, 2025
概括
本研究探讨了使用大型语言模型 (LLM) 的社交机器人进行协作讲故事. 这种新的系统整合了物理物体和富有表现力的机器人表演,显示了用户对交互式叙事的高度接受.
科学领域:
- 人与机器人的交互
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 在虚拟代理中越来越多地用于协作讲故事.
- 在讲故事的社会机器人中,LLM的整合仍然未被充分探索.
- 讲故事是社交机器人吸引观众的传统方法.
研究的目的:
- 介绍一款新型多式联运合作讲故事系统的初步步骤.
- 调查LLM用于自主故事生成和适应的使用.
- 评估LLM在社交机器人交互式讲故事中的可用性和成熟度.
主要方法:
- 开发了一个多式联络系统,用户可以与社交机器人Pepper共同创建故事.
- 利用基于YOLO的视觉系统进行对象识别和叙事集成.
- 采用拉玛模型进行零拍摄故事生成和改编.
- 通过机器人的表达性手势,情感线索和语音调制来增强沉浸感.
主要成果:
- 该系统成功地将用户提供的物理对象集成到叙事中.
- 拉玛模型在零拍摄环境中自主生成和调整故事.
- 社交机器人Pepper通过表达性的非语言和语言线索来执行故事.
- 用户反表明,合作讲故事系统的接受程度很高.
结论:
- 在人机交互中,LLM显示出自主故事生成的前景.
- 多模式输入,包括物理对象,增强了协作式讲故事体验.
- 表达式的机器人表演对于增加沉浸和用户接受交互式叙述至关重要.
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