大模型
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面向 MyBuddy 人形机器人的基于大语言模型的对话式 AI 知识助手
该 arXiv 论文（cs.RO/cs.AI，2026 年 9 月 21 日提交）提出并实现了一个面向 Raspberry-Pi 驱动的 13 轴 MyBuddy 人形机器人的 LLM 对话式知识助手。系统整合 LLM 驱动的语言理解与推理、实时语音识别、通过 Wikipedia 和 arXiv 等可扩展互联网引擎的知识检索、灵活对话管理以及自然语音合成，目标是支持多轮连续对话与情感支持型人机交互，以替代传统规则式对话系统和有限知识库。摘要未给出实验数据或性能评测。
AI推荐理由 arXiv 单作者论文，摘要明确描述了在 Raspberry-Pi 驱动的 13 轴 MyBuddy 人形机器人上集成 LLM 语言理解、实时语音识别、Wikipedia/arXiv 知识检索、对话管理与语音合成，属于可验证的系统实现描述；但仅有摘要，无实验数据、评测指标或对比结果，无法判断实际效果，且作者与机构信息有限，兴趣点集中在小型人形机器人+LLM 对话助手的落地尝试。
62
正文
Computer Science > Robotics
arXiv:2609.24742 (cs)
[Submitted on 21 Sep 2026]
Title:LLM-based Conversational AI Knowledge Assistant for MyBuddy Humanoid Robot
Authors:Hanxiao Chen
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Abstract: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.
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Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as:
arXiv:2609.24742 [cs.RO]
(or
arXiv:2609.24742v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.24742
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hanxiao Chen [view email]
[v1]
Mon, 21 Sep 2026 15:11:06 UTC (7,023 KB)
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