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arXiv · 2403.10672

Riemannian Flow Matching Policy for Robot Motion Learning

Max Braun·Noémie Jaquier·Leonel Rozo·Tamim Asfour
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Abstract

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.

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Max Braun, Noémie Jaquier, Leonel Rozo, Tamim Asfour. 2024-03-15. Riemannian Flow Matching Policy for Robot Motion Learning. https://arxiv.org/abs/2403.10672

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