FRMD: fast robot motion diffusion via trajectory-level consistency distillation

Foundation models for embodied artificial intelligence (Embodied AI) increasingly adopt diffusion modules as the action generation core of vision–language–action (VLA) policies, but the diffusion module’s iterative denoising imposes prohibitive inference latency for real-time deployment. We address this bottleneck in isolation by rethinking the diffusion action generation module itself. We present Fast Robot Motion Diffusion (FRMD) , a fast robot motion diffusion framework that (i) operates in trajectory-parameter space by predicting movement-primitive coefficients in a low-dimensional manifold, and (ii) collapses multi-step sampling into a single inference step via trajectory-level consistency distillation over the probability-flow ordinary differential equation (ODE). Concretely, FRMD replaces stepwise action generation with a one-pass mapping from noise to full trajectories, followed by a fixed-cost basis expansion; this reduces policy latency from hundreds to tens of milliseconds without modifying upstream vision or language encoders. On standard robotic manipulation task benchmarks, FRMD attains 7 times faster than the vanilla diffusion policy and 10 times faster than the state-of-the-art MPD method, while matching the task success of multi-step diffusion policies. By targeting the diffusion component used throughout VLA systems, FRMD provides a plug-in, latency-optimized motion generator that preserves the advantages of diffusion and makes real-time embodied AI feasible.