Robot Learning

Noise Schedule

A noise schedule is the sequence of noise levels used in a diffusion model, defining how much Gaussian noise is added at each step of the forward process and, correspondingly, how denoising proceeds during sampling. Common choices include linear and cosine schedules over the variance parameters. In diffusion policies, the schedule and the number of denoising steps trade off action quality against inference latency.

Why it matters for physical AI

Diffusion-based robot policies must denoise actions within a real-time control budget, so schedule design and step-reduction techniques like DDIM directly determine achievable control rates.