Robot Learning

Consistency Policy

Consistency Policy is a visuomotor policy approach (Prasad et al., 2024) that applies consistency distillation to diffusion policies, training a student network to map any point on the diffusion trajectory directly to its endpoint so actions are generated in one or a few inference steps. It retains the multimodal expressiveness of diffusion policies while cutting inference latency by an order of magnitude, enabling high-rate control on compute-constrained robots.

Why it matters for physical AI

Diffusion policies' iterative sampling is often too slow for reactive control, so consistency-style distillation is a key path to running expressive generative policies at real-time rates on embedded hardware.