提出快速稳健的机器人视觉运动策略，兼具高效训练与推理优势。
Fast and Robust Visuomotor Riemannian Flow Matching Policy
- 基于流匹配框架，直接建模机器人状态的黎曼流形空间动态。
- 在10个仿真与真实任务中实现比扩散策略更快的推理与更优性能。
- 通过稳定性理论提升鲁棒性，适合高维复杂控制场景应用。
基于扩散的视觉运动策略通过有效融合视觉信息与高维多模态动作分布，在学习复杂机器人任务方面表现出色。然而，扩散模型常因代价高昂的去噪过程导致推理缓慢，或依赖近期蒸馏方法带来复杂的序列训练。本文提出黎曼流匹配策略（RFMP），继承流匹配（FM）易于训练和快速推理的优点。此外，RFMP天然融入了真实机器人应用中常见的几何约束，因为机器人状态位于黎曼流形上。为增强鲁棒性，我们进一步提出稳定型RFMP（SRFMP），利用LaSalle不变性原理，使FM动力学具备对目标黎曼分布支撑集的稳定性。在十个仿真与真实世界任务上的严格评估表明，RFMP能在欧氏空间与黎曼空间上成功学习并合成复杂感知-运动策略，具有高效的训练与推理阶段，性能优于扩散策略与一致性策略。
原文摘要 · Abstract (English)
Diffusion-based visuomotor policies excel at learning complex robotic tasks by effectively combining visual data with high-dimensional, multi-modal action distributions. However, diffusion models often suffer from slow inference due to costly denoising processes or require complex sequential training arising from recent distilling approaches. This paper introduces Riemannian Flow Matching Policy (RFMP), a model that inherits the easy training and fast inference capabilities of flow matching (FM). Moreover, RFMP inherently incorporates geometric constraints commonly found in realistic robotic applications, as the robot state resides on a Riemannian manifold. To enhance the robustness of RFMP, we propose Stable RFMP (SRFMP), which leverages LaSalle's invariance principle to equip the dynamics of FM with stability to the support of a target Riemannian distribution. Rigorous evaluation on ten simulated and real-world tasks show that RFMP successfully learns and synthesizes complex sensorimotor policies on Euclidean and Riemannian spaces with efficient training and inference phases, outperforming Diffusion Policies and Consistency Policies.
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