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

新的Kinematic MeanFlow策略提升机器人基础模型动作生成能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

研究人员开发了Kinematic MeanFlow (K-MF)，一种旨在增强机器人基础模型 (RFMs) 的新型一步式动作生成策略。该新策略通过解耦时间导数项来解决MeanFlow性能崩溃的问题，使其能够更好地捕捉早期和晚期去噪动态。K-MF使RFMs能够实现一步式动作生成，提高效率，在测试中动作头延迟最多可降低74.4%，端到端延迟最多可降低54.9%。 AI

影响这项研究通过降低推理延迟，可能带来更快、更高效的机器人控制系统。

排序理由该集群包含一篇详细介绍机器人基础模型新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Jiawei Fan, Sifeng Wang, Yuqing Hou, Anbang Yao · 2026-10-02 04:00

Kinematic MeanFlow：机器人基础模型的一步动作生成策略

arXiv:2610.00864v1 Announce Type: cross Abstract: 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 goa…

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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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