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English(EN) JoyInside-VLM-Agent: Product-Conditioned Multimodal Policy Learning for Embodied Agents

新型多模态智能体JoyInside-VLM-Agent增强具身机器人和智能硬件

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了JoyInside-VLM-Agent，这是一种ReAct风格的多模态智能体，专为机器人和智能硬件中的具身智能体设计。该智能体利用产品条件轨迹构建和分阶段策略学习，并包含一个闭环沙箱，用于生成包含目标修正和故障恢复的交互轨迹。训练过程包括监督微调、多教师on-policy蒸馏以及采用新颖的DPER方法进行证据路由的强化学习。JoyInside-VLM-Agent在BFCL v4基准测试中取得了强劲表现，并在JoyInside-Devices和JoyInside-Robotics的指标上领先，展示了视觉基础搜索和重新规划的能力。 AI

影响 引入了一种新的具身AI智能体架构和训练方法，有望提高机器人和智能设备的交互能力。

排序理由 该集群描述了一篇关于具身系统新AI智能体的研究论文。[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 →

新型多模态智能体JoyInside-VLM-Agent增强具身机器人和智能硬件

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该集群描述了一篇关于具身系统新AI智能体的研究论文。[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) · Gangyi Li, LuYao Wang, Jingfu Sun, Jingyi Yuan, Hang Liu, Hao Zong, Yujia Hu, Tingting Gao, Jianyong Wang, Xu Yang ·

    JoyInside-VLM-Agent: 面向具身智能体的产品条件多模态策略学习

    arXiv:2610.12957v1 Announce Type: cross Abstract: Embodied agents must translate multimodal observations and user requests into executable actions while adapting to product constraints, changing goals, and execution feedback. We present JoyInside-VLM-Agent, a ReAct-style multimod…

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