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.

我们引入了一种名为 Riemannian Flow Matching Policies（RFMP）的新型模型，用于学习和合成机器人视觉运动策略。RFMP 利用了流匹配方法的高效训练和推理能力，具备编码机器人任务中常见的高维多模态分布和简单快速推理过程的优点。我们展示了 RFMP 在基于状态和基于视觉条件的机器人运动策略中的适用性。值得注意的是，由于机器人状态存在于 Riemann 流形上，RFMP 固有地融合了几何意识，这对于真实机器人任务至关重要。为了评估 RFMP，我们进行了两个概念验证实验，并将其性能与 Diffusion Policies 进行了比较。尽管两种方法都成功地学习了考虑的任务，但结果表明，RFMP 提供了更平滑的动作轨迹并具有显着较低的推理时间。