用大模型自动生成机器人叙事行为，实现全程自动化
Robot guide with multi-agent control and automatic scenario generation with LLM
- 多智能体系统结合大模型自动生成机器人行为场景
- 在MENTOR-1机器人上实现自然丰富的长期叙事交互
- 适合需要长期对话与非语言行为的社交机器人研发
本文提出一种混合社会机器人控制架构，以克服传统方法中行为脚本手动同步动作与文本、且仅关注短对话响应的局限。该系统融合多智能体资源管理系统与基于大语言模型的自动行为场景生成机制，实现了对机器人非语言行为的文本与指令的自动化准备，支持长篇叙事，并有效解决多执行机制间的资源冲突。在MENTOR-1导览机器人上的测试表明，系统可自动生成场景，相比现有方法展现出更自然、更丰富的行为表现。该方法通过高效的资源管理，实现了场景准备与执行的全流程自动化，显著提升了社交机器人在长期故事讲述任务中的交互质量。
原文摘要 · Abstract (English)
The article describes the development of a hybrid social robot control architecture to overcome the limitations of traditional approaches, where behavior scripts manually synchronize the robot's actions and text, and existing methods focus primarily on short dialogue responses. The architecture of the proposed system combines a multi-agent resource management system with automatic generation of behavior scenarios based on large language models. This system automates the preparation of text and commands for the robot's non-verbal behavior for extended narratives and resolves resource conflicts between multiple execution mechanisms. The system was tested on the MENTOR-1 tour guide robot, for which it successfully generated scenarios automatically and demonstrated more natural and rich behavior compared to existing approaches. The proposed approach provides full automation of both scenario preparation and execution through efficient resource management, enhancing the quality of social robot interaction in long-term storytelling tasks.
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