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English(EN) MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping

MANGO-Grasp 推进跨具身机器人抓取

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了MANGO-Grasp，一个用于不同机器人手之间灵巧抓取的新颖框架。该系统利用各向异性马氏体场，并使用面向几何的3D高斯基元来表示物体，以更好地捕捉表面细节和交互动态。在基准测试中，MANGO-Grasp 与现有方法相比表现出卓越的性能，在模拟和真实世界实验中均取得了显著改进，并实现了到未见过机器人手的零样本迁移。 AI

影响 这项研究可能带来跨不同硬件平台的更具适应性和效率的机器人操作。

排序理由 该集群包含一篇详细介绍机器人抓取新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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MANGO-Grasp 推进跨具身机器人抓取

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该集群包含一篇详细介绍机器人抓取新方法的论文。[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) · Heng Zhang, Kevin Yuchen Ma, Mike Zheng Shou, Weisi Lin, Yan Wu ·

    MANGO-Grasp：面向几何的 3D 高斯马氏距离场，用于跨具身灵巧抓取

    arXiv:2608.02014v2 Announce Type: replace-cross Abstract: Cross-embodiment dexterous grasping aims to synthesize stable grasps across heterogeneous multi-fingered hands with little or no embodiment-specific tuning. Existing interaction-centric methods achieve promising results, b…

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