This paper propose a latent flow matching model using time-conditioned Fourier layers to capture multi-scale modes with high fidelity in both spatial and spectral characteristics to forecast high-dimensional, PDE-governed dynamics over long time horizons.
Something interesting I learned from this paper:
- Flow matching can be used for timeseries forecasting
- Flow matching can be used on the top of latent space
Since robotic trajectories are also timeseries, and contains both spatial and spectral characteristics, and high-dimensional. Can we use TEMPO in trajectory generation?