Robot Learning

Variational Inference

Variational inference is a technique for approximating an intractable posterior distribution by optimizing the parameters of a simpler distribution to minimize its KL divergence from the true posterior, usually by maximizing the evidence lower bound (ELBO). It converts Bayesian inference into an optimization problem that scales to deep networks, and it underlies variational autoencoders, latent-variable policies, and several model-based reinforcement learning methods.

Why it matters for physical AI

Robots must reason under uncertainty from noisy, partial observations, and variational methods provide the tractable machinery behind learned state estimators, world models such as Dreamer, and latent-variable policy architectures.