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NavGPT-3 系统将大型语言模型与具身智能体的动作策略相结合

研究人员开发了 NavGPT-3 系统,该系统将语言模型与具身智能体的动作策略相结合。这种集成通过将推理、动作和监控作为单独的线程运行来实现分层导航,并由一个运行时管理,该运行时可以响应现实世界事件将控制权切换到机器人的运动。该系统的动作策略 NavGPT VLA 在超过 1900 万个示例上进行了训练,在 R2R-CE 和 RxR-CE 等导航基准测试中取得了最先进的成果,在成功率和路径保真度方面与人类表现相当。 AI

影响 这项研究通过创建一个将高级推理与低级物理控制相结合的系统,推动了具身人工智能的发展,有望实现更复杂的自主代理。

排序理由 这是一篇详细介绍具身智能体新系统的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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NavGPT-3 系统将大型语言模型与具身智能体的动作策略相结合

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这是一篇详细介绍具身智能体新系统的研究论文。[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) · Gengze Zhou, Yicong Hong, Jiazhao Zhang, Xunyi Zhao, Jian Zhou, Zixing Lei, Zun Wang, Chongyang Zhao, Xionghui Chen, Stephen Gould, Anton van den Hengel, Qi Wu ·

    NavGPT-3: 在分层导航运行时中利用上下文

    arXiv:2610.10787v1 Announce Type: cross Abstract: Language models trained with long-horizon agentic reinforcement learning can generalize knowledge through reasoning, express precise actions, and pursue goals over many steps, raising the ceiling on what an embodied agent can unde…