Riemannian Flow Matching Policy for Robot Motion Learning
<|reference_start|>Riemannian Flow Matching Policy for Robot Motion Learning: 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.<|reference_end|>
@article{braun2024riemannian,
    title={Riemannian Flow Matching Policy for Robot Motion Learning},
    author={Max Braun, No'emie Jaquier, Leonel Rozo, Tamim Asfour},
    journal={arXiv preprint arXiv:2403.10672},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10672},
    primaryClass={cs.RO cs.LG}
}