English(EN) Elastic-WAM: Adaptive Spatiotemporal Imagination for Efficient Robot Control

Elastic-WAM 通过自适应时空想象增强机器人控制

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员开发了 Elastic-WAM，这是一种通过自适应管理时空想象来增强机器人控制的新方法。该方法通过在时间去噪和空间未来区域之间动态分配精炼资源，解决了世界-动作模型（World-Action Models）的计算开销问题。Elastic-WAM 使用学习到的时空精炼需求场来指导稀疏世界精炼，从而在模拟和现实世界的操纵任务中提高了任务成功率并降低了推理延迟。 AI

影响通过优化预测模型的计算资源，这项研究可能带来更高效、更有效的机器人控制系统。

排序理由详细介绍机器人控制新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Haitian Li, Tianfu Li, Xuxi Zhao, Zizheng Liu, Yangcheng Yu, Yiwen Hou, Zeyu Xiao, Mutian Xu, Ziqian Yang, Haonan Chen, Lin Shao · 2026-11-11 05:00

Elastic-WAM：高效机器人控制的自适应时空想象

arXiv:2611.14257v1 Announce Type: cross Abstract: World-Action Models improve robot control by predicting future visual dynamics, but iterative video denoising incurs substantial inference overhead. Recent approaches reduce this cost through partial future denoising, yet typicall…

本文如何被排名

报道来源 [1]

Elastic-WAM：高效机器人控制的自适应时空想象

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