Robot Learning

Gaussian Process (GP)

A Gaussian process is a nonparametric Bayesian model that defines a distribution over functions, providing predictions with calibrated uncertainty from limited data via a kernel encoding smoothness assumptions. In robotics, GPs have modeled dynamics for data-efficient model-based reinforcement learning, most famously in PILCO, as well as terrain, residual dynamics errors, and Bayesian optimization objectives for controller tuning. Exact inference scales cubically with data, limiting GPs to modest datasets.

Why it matters for physical AI

Sample efficiency and honest uncertainty are perpetual concerns when learning on physical hardware; GPs remain a benchmark for data-efficient model learning and a tool for safe exploration and controller auto-tuning.