Riemannian Flow Matching Policy for Robot Motion Learning
This work addresses robot motion learning for robotic tasks, but it is incremental as it builds on existing flow matching methods with geometric adaptations.
The paper tackled learning robot visuomotor policies by introducing Riemannian Flow Matching Policies (RFMP), which leverages flow matching for efficient training and inference, resulting in smoother action trajectories and significantly lower inference times compared to Diffusion Policies.
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.