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English(EN) From Language to Drives: A Multimodal Framework for Aligning Autonomous Robots with Human Purposes

新框架使用LLM将机器人行为与人类意图对齐

研究人员开发了一个多模态框架,利用大型语言模型(LLM)和视觉语言模型(VLM)将自主机器人与人类意图对齐。该系统将人类的自然语言意图转化为机器人的任务和驱动,指导机器人的行为。该框架采用三层设计,用于语义对齐、任务生成和驱动建模,形成一个双向循环,以实现有效的人机交互。在模拟和物理机器人上的实验表明,该方法成功生成了与人类意图一致的任务和行为。 AI

影响 该框架可以实现更直观的人机协作,以及更安全地部署自主系统。

排序理由 该集群包含一篇详细介绍新研究框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新框架使用LLM将机器人行为与人类意图对齐

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该集群包含一篇详细介绍新研究框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Diana Cerdas-Vargas, Alejandro Romero, Richard J. Duro ·

    从语言到驱动:一个多模态框架,用于将自主机器人与人类目标对齐

    arXiv:2610.35629v1 Announce Type: cross Abstract: This work explores a multimodal approach to the alignment of the behavior of open-ended learning autonomous robots with human purposes through the use of large language models as cultural enablers. While autonomous robots can gene…