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English(EN) ActiveScale: Scaling Active Perception for Robots across Model, Data, and Hardware

新框架ActiveScale增强机器人主动感知能力

研究人员开发了ActiveScale,一个旨在改进机器人主动感知能力的新框架。该系统整合了模型、数据和硬件的进步,使机器人能够跨不同视角进行推理并收集信息性观察。ActiveScale通过历史视频数据和显式的相机姿态监督来增强视觉-语言-动作模型,促进对场景的连贯理解。该框架还包括一个使用广泛的以自我为中心和机器人数据的中期训练方案,以及一个名为AMP的可扩展数据收集机器人平台。 AI

影响 通过改进视觉场景理解和数据采集来增强机器人操作能力。

排序理由 该集群描述了一篇在arXiv上发表的研究论文,详细介绍了一个新的机器人框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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

新框架ActiveScale增强机器人主动感知能力

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该集群描述了一篇在arXiv上发表的研究论文,详细介绍了一个新的机器人框架。[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) · Shuai Zhou, Kaisheng Pang, Wenxuan Song, Wenjie Zhang, Xinhu Zheng, Haoang Li ·

    ActiveScale:跨越模型、数据和硬件实现机器人的主动感知扩展

    arXiv:2609.18514v1 Announce Type: cross Abstract: Active perception is essential for robotic manipulation when fixed viewpoints leave task-relevant information occluded or unobserved. However, enabling vision-language-action (VLA) models to reason across changing viewpoints and a…