Riemannian Flow Matching Policy for Robot Motion Learning

• URL: http://arxiv.org/abs/2403.10672v2
• Date: Tue, 27 Aug 2024 11:13:43 GMT
• Title: Riemannian Flow Matching Policy for Robot Motion Learning
• Authors: Max Braun, Noémie Jaquier, Leonel Rozo, Tamim Asfour,
• Abstract summary: We introduce a novel model for learning and synthesizing robot visuomotor policies. We show that RFMP provides smoother action trajectories with significantly lower inference times.
• Score: 5.724027955589408
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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