We introduce Riemannian Flow Matching Policies (RFMP), a novel model for learning and synthesizing robot visuomotor policies. RFMP leverages the efficient training and inference capabilities of flow matching methods. By design, RFMP inherits the strengths of flow matching: the ability to encode high-dimensional multimodal distributions, commonly encountered in robotic tasks, and a very simple and fast inference process. We demonstrate the applicability of RFMP to both state-based and vision-conditioned robot motion policies. Notably, as the robot state resides on a Riemannian manifold, RFMP inherently incorporates geometric awareness, which is crucial for realistic robotic tasks. To evaluate RFMP, we conduct two proof-of-concept experiments, comparing its performance against Diffusion Policies. Although both approaches successfully learn the considered tasks, our results show that RFMP provides smoother action trajectories with significantly lower inference times.

翻译：本文提出黎曼流匹配策略（RFMP），一种用于学习和合成机器人视觉运动策略的新型模型。RFMP充分利用流匹配方法高效训练与推理的优势。通过精心设计，RFMP继承了流匹配的核心特性：能够编码机器人任务中常见的高维多模态分布，并具备极其简洁快速的推理流程。我们验证了RFMP在基于状态和视觉条件两类机器人运动策略中的适用性。特别值得注意的是，由于机器人状态存在于黎曼流形上，RFMP天然具备几何感知能力，这对实现真实机器人任务至关重要。为评估RFMP性能，我们开展了两项概念验证实验，并将其与扩散策略进行对比分析。实验结果表明，两种方法均能成功学习目标任务，但RFMP能生成更平滑的动作轨迹，且推理时间显著降低。