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English(EN) Temporal Visuo-Tactile Learning for Dexterous Grasp Stability

机器人利用先进的视觉和触觉技术学习稳定抓取 · 跟踪3个来源

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员正在开发先进的机器人抓取方法，重点是提高稳定性和准确性。一种方法使用时间视觉触觉学习和高分辨率触觉传感器来预测抓取稳定性，在真实机器人上的成功率提高了 10.5 个百分点。另一种方法 StableGrasp 通过在可微分模拟器中优化手部几何形状和控制力，从单个图像重建物理上稳定的人手抓取。第三种技术 Volumetric Contact (VolCo) 使用体积网格表示接触，从而实现更精确的手部恢复并生成更紧密的抓取，减少穿透。 AI

影响 这些机器人抓取技术的进步可能导致在制造、物流甚至家庭环境中出现更强大、更多功能的机器人。

排序理由 arXiv 上发表了多篇关于机器人抓取新方法的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

机器人利用先进的视觉和触觉技术学习稳定抓取 · 跟踪3个来源

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arXiv 上发表了多篇关于机器人抓取新方法的学术论文。
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报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Ken Nakahara, Aleksei Buvailik, Prokhor Kotov, Roberto Calandra ·

    面向灵巧抓握稳定性的时间性视触觉学习

    arXiv:2610.10283v1 Announce Type: cross Abstract: Humans can grasp everyday objects with almost perfect success rates using fingertip tactile feedback, yet much of the robotic grasping literature emphasizes vision-based grasp selection with parallel grippers. In this work, we sys…

  2. arXiv cs.CV TIER_1 English(EN) · Han Jiang, Etienne Vouga, Qixing Huang, Georgios Pavlakos ·

    StableGrasp：从单张图像重建物理上稳定的人手抓握

    arXiv:2610.09195v1 Announce Type: new Abstract: Reconstructing a physically stable human grasp from a single RGB image is challenging because physically modeling grasps is itself difficult, and the problem requires estimating not only a visually constrained hand pose but also a c…

  3. arXiv cs.CV TIER_1 English(EN) · Zhuo Chen, Yihua Cheng, Ale\v{s} Leonardis, Hyung Jin Chang ·

    VolCo：用于高保真人体抓取生成的体积接触

    arXiv:2610.10197v1 Announce Type: new Abstract: Accurate contact modeling is fundamental to understanding hand-object interaction, yet existing contact representations are typically restricted to object surfaces and rely on hand-crafted rules to recover contact details, leading t…

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