面向视觉运动流匹配的以对象为中心的调节

中文摘要 AI 辅助

机器人视觉运动策略通常被构建为自回归模型、基于扩散的模型，或更近期地，流匹配模型。其中，动作到动作（A2A）流匹配通过从历史动作先验而非随机噪声初始化生成过程，提高了推理效率。然而，过时的历史运动模式和纠缠的全局视觉表示可能共同降低在空间分布外（OOD）偏移和视觉干扰物下的鲁棒性。在本工作中，我们提出SlotFlow，一种以对象为中心的流匹配策略，用于鲁棒的视觉运动操作。SlotFlow将场景观察解耦为语义（“是什么”）特征和轻量级图像平面空间（“在哪里”）线索，以提供对象感知的策略调节和当前状态基础。语义表示抑制了不相关的背景相关性，而空间线索改善了对偏移对象配置的适应性。广泛的仿真和真实世界实验表明，在视觉干扰物和严重空间扰动下，鲁棒性得到提升，同时保持了A2A的低步推理效率。受控初始化和感知消融进一步识别出以对象为中心的基础作为增益的主要来源，并表明它补充而非取代了有用的历史运动先验。

英文摘要

Robot visuomotor policies are commonly formulated as autoregressive, diffusion-based, or more recently, flow matching models. Among them, Action-to-Action (A2A) flow matching improves inference efficiency by initializing generation from historical action priors rather than stochastic noise. However, stale historical motion patterns and entangled global visual representations can jointly reduce robustness under spatial out-of-distribution (OOD) shifts and visual distractors. In this work, we propose SlotFlow, an object-centric flow matching policy for robust visuomotor manipulation. SlotFlow decouples scene observations into semantic ("what") features and lightweight image-plane spatial ("where") cues to provide object-aware policy conditioning and current-state grounding. The semantic representation suppresses irrelevant background correlations, while the spatial cue improves adaptation to shifted object configurations. Extensive simulation and real-world experiments demonstrate improved robustness under visual distractors and severe spatial perturbations while preserving the low-step inference efficiency of A2A. Controlled initialization and perception ablations further identify object-centric grounding as a major source of the gains and show that it complements, rather than replaces, useful historical motion priors.

发表机构

• University of Chinese Academy of Sciences（中国科学院大学）
• Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）

机构由 AI 辅助整理，请以论文原文为准。

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Accepted to the 10th Conference on Robot Learning (CoRL 2026)