Robot Learning

Diffusion Policy

Diffusion Policy is a visuomotor policy class that generates robot action sequences by iteratively denoising samples conditioned on visual observations, introduced by Chi et al. in 2023. Modeling the action distribution generatively lets it capture multimodal demonstrations that regression-based behavior cloning averages into invalid actions, and predicting action chunks over a receding horizon yields temporally consistent motion. It substantially outperformed prior imitation methods across simulated and real manipulation benchmarks.

Why it matters for physical AI

Diffusion-based action heads have become a default choice for demonstration-trained manipulation and appear inside several robot foundation models. Their inference latency motivates ongoing work on distillation and faster samplers for high-rate control.