Robot Learning

Classifier-Free Guidance (CFG)

Classifier-free guidance is a sampling technique for conditional diffusion models, introduced by Ho and Salimans (2022), in which the model is trained with the conditioning signal randomly dropped, and at inference the conditional and unconditional score estimates are extrapolated to strengthen adherence to the condition. In robot learning it sharpens goal- or language-conditioning of diffusion policies, trading sample diversity for conditioning fidelity via a guidance weight.

Why it matters for physical AI

Diffusion-based robot policies use guidance to make behavior follow language instructions or goals more reliably, and the guidance weight is a practical knob for tuning precision versus diversity at deployment.