外观
论文arXiv 2610.2219313 天前
MOSS：用于机器人流匹配策略的面向模式源塑形
原文链接：MOSS: Mode-Oriented Source Shaping for Robot Flow Matching Policies（arXiv 2610.22193 · 发布日期：2026-10-15）
MOSS: Mode-Oriented Source Shaping for Robot Flow Matching Policies
针对生成式操作策略难以主动控制执行模式，MOSS通过模式特定高斯源选择生成起点，并在仿真和真实操作任务上验证。
核心要点
- 同一观测可对应多种可行动作，扩散和流匹配虽能建模但采样模式不可控。
- MOSS将模式选择转化为生成起点选择，构造语义对齐的模式特定高斯源。
- 模态强度估计器预测局部多模态程度，自适应调节各模式源的几何关系。
- 在6个仿真和3个真实任务中，目标模式引导率最高100%，少数模式超90%。
验证范围与局限：验证范围为6个仿真和3个真实世界操作任务。
英文摘要 (Abstract)
In robotic manipulation, the same observation often admits multiple feasible actions. Generative policies such as diffusion and flow matching naturally capture such multimodal behaviors, yet which mode is executed is left to random sampling and cannot be actively controlled. We propose Mode-Oriented Source Shaping (MOSS), which turns mode selection into an explicit choice of where generation starts. We first reveal, both empirically and theoretically, that deterministic flow transport induces mode-dependent structure in the source space. Under ideal deterministic ODE transport, distinct behavioral modes have distinct source-space preimages, while practical approximation errors can blur their boundaries due to imbalanced data, limited model capacity, and few inference steps. Motivated by this source-space structure, MOSS constructs semantically aligned mode-specific Gaussian sources that encode mode identity in the initial noise. A modality-strength estimator predicts the local degree of multimodality, which adaptively controls the geometry of the mode-specific sources. It contracts them toward a shared Gaussian where behaviors are similar and separates them near behavioral branching points. During deployment, the desired behavior is selected at inference simply by sampling from the corresponding component. Across 6 simulated and 3 real-world manipulation tasks, MOSS steers to the target mode up to 100% of the time with high task success, recovers minority modes at over 90% guidance rate under a 10:90 imbalance dataset, and surpasses mode-conditioned flow policy by 43.3 points of guidance rate on real robots.
作者：Zirun Ma, Jindou Jia, Tuo An, Jiaqi Bai, Jiahua Dong, Jianfei Yang
来源
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