基于LLM的MyBuddy人形机器人对话式AI知识助手
LLM-based Conversational AI Knowledge Assistant for MyBuddy Humanoid Robot
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中文总结 AI 辅助
针对人形机器人对话能力受限问题，提出基于LLM的MyBuddy人形机器人对话式AI知识助手，集成语音识别、知识检索与合成，实现多轮连续对话和情感支持交互。
中文摘要 AI 辅助
人形机器人正日益普及并被开发用于以人为中心的应用，然而其提供智能对话和自然交互式知识辅助的能力仍受限于传统的基于规则的对话系统、预定义响应和有限的知识库。大型语言模型（LLM）已成为实现自然、自适应和上下文感知的人机交互（HRI）的强大基础，通过使机器人能够理解自然语音、推理复杂查询、保持高质量对话上下文并生成知识丰富的响应，为解决上述局限性提供了重要机会。在本工作中，我们首次提出并实现了一个基于LLM的多功能对话式AI知识助手，用于由树莓派驱动的13轴MyBuddy人形机器人，该助手集成了LLM驱动的语言理解和AI推理、实时语音识别、通过可扩展的互联网引擎（如Wikipedia、arXiv）访问的知识检索、灵活的对话管理以及自然语音合成，以实现更智能的多轮连续对话和先进的情感支持型人机交互。
英文摘要
Humanoid robots are increasingly being popular and developed for human-centered applications, yet their ability to provide intelligent conversations and natural interactive knowledge assistance remains constrained by traditional rule-based dialogue systems, pre-defined responses and limited knowledge repositories. Large language models (LLMs) have emerged as a powerful foundation for enabling natural, adaptive, and context-aware Human-Robot Interaction (HRI), which provides a significant opportunity to address such limitations by enabling robots to understand natural speech language, reason over complicated queries, maintain high-quality conversational context, and generate knowledge-rich responses. In this work, we originally present and implement an LLM-based versatile Conversational AI Knowledge Assistant for the Raspberry-Pi-powered 13-Axis MyBuddy humanoid robot, which integrates LLM-driven language understanding and AI reasoning with real-time speech recognition, knowledge retrieval via extensible access of internet engines (e.g., Wikipedia, arXiv), flexible dialogue management, and natural speech synthesis to enable much more intelligent multi-turn continuous conversations and advanced emotional-support Human-Robot Interaction.
发表机构
- The University of Tokyo（东京大学）
机构由 AI 辅助整理，请以论文原文为准。
