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English(EN) HiWE: Hierarchical World Knowledge Model with Visual Keypoint Enhancement for Zero-Shot 3D Path Planning

新型HiWE模型赋能机器人实现零样本3D路径规划

研究人员开发了HiWE,一个层次化世界知识模型,旨在增强机器人的零样本3D路径规划能力。该模型通过使用PointVLM将物体与图像坐标和深度测量关联起来,以创建语义3D表示,从而将视觉基础与基于语言的规划相结合。然后,一个名为3DLLM的语言规划器利用此表示来指定机器人运动和抓取器命令,使机器人能够在没有特定任务演示训练的情况下执行操作任务。 AI

影响 使机器人能够在没有事先特定任务训练的情况下,在3D环境中执行复杂的操作任务。

排序理由 该条目描述了一篇详细介绍机器人路径规划新模型的学术论文。[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 →

新型HiWE模型赋能机器人实现零样本3D路径规划

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该条目描述了一篇详细介绍机器人路径规划新模型的学术论文。[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) · Guoqing Ma, Mingqi Yuan, Chen Gao, Jiayu Chen, Shan Yu ·

    HiWE:具有视觉关键点增强的层次化世界知识模型,用于零样本3D路径规划

    arXiv:2609.39323v1 Announce Type: cross Abstract: Robot demonstration generation requires a system to identify where an interaction should occur, plan a feasible motion, and execute the required contact. HiWE connects these decisions through a point-based interface between visual…