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English(EN) VLA-ULAP: Interleaving Cloud VLA Calls with Ultra-Lightweight Local Action Prediction at the Edge

新的VLA-ULAP系统通过减少云调用来提高边缘AI效率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了VLA-ULAP系统，该系统结合了基于云的视觉-语言-动作（VLA）模型和一个轻量级的本地预测器，以提高边缘设备的效率和速度。这种方法显著减少了所需的远程VLA调用次数，在保持高成功率的同时降低了推理时间和能耗。VLA-ULAP在NVIDIA Jetson Orin Nano等硬件上展示了其有效性，在模拟和物理环境中均优于其他方法。 AI

影响 通过优化VLA模型的使用，降低了边缘AI应用的计算负载和延迟。

排序理由 该项目是一篇详细介绍新系统及其性能的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新的VLA-ULAP系统通过减少云调用来提高边缘AI效率

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该项目是一篇详细介绍新系统及其性能的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Deyu Cao, Ryuji Oi, Kosuke Matsushima, Yuxuan Pan, Ziheng Wang, Daichi Fujiki, Atsutake Kosuge ·

    VLA-ULAP：在边缘端交错进行云端VLA调用与超轻量级本地动作预测

    arXiv:2609.18663v1 Announce Type: cross Abstract: Billion-parameter vision--language--action (VLA) policies demand substantial onboard power, while communication delays in remote inference hinder timely responses. We propose VLA-ULAP, which interleaves remote VLA calls with an Ul…

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