English(EN) Kinematic MeanFlow: One-Step Action Generation Policy for Robotic Foundation Models

Kinematic MeanFlow 将机器人模型延迟最多降低 74%

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 00:00

研究人员开发了 Kinematic MeanFlow (K-MF)，这是一种新颖的一步动作生成策略，旨在降低机器人基础模型 (RFMs) 的推理延迟。K-MF 通过解耦时间导数项来解决 MeanFlow 框架中观察到的性能问题，使其能够更好地捕捉早期和晚期去噪动态。这一新策略使 RFMs 能够实现一步动作生成，并显著降低延迟，在各种任务中优于多步流匹配。 AI

影响降低机器人基础模型的推理延迟，可能实现更快、更高效的机器人操作。

排序理由这是一篇详细介绍机器人基础模型新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 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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Hugging Face Daily Papers TIER_1 English(EN) · 2026-10-01 00:00

Kinematic MeanFlow：机器人基础模型的一步动作生成策略

In this paper, we study how to achieve one-step action generation in Robotic Foundation Models (RFMs), aiming to overcome the high inference latency of multi-step flow matching. MeanFlow provides a promising framework for this goal, yet its direct application leads to performance…

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Kinematic MeanFlow：机器人基础模型的一步动作生成策略

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