Gaussian process
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A Gaussian process (GP) is a probabilistic machine learning framework that defines a distribution over functions, allowing predictions to be made along with principled uncertainty estimates. Rather than fitting a single deterministic model, a GP treats any finite set of function values as jointly Gaussian-distributed, characterized entirely by a mean function and a covariance (kernel) function that encodes assumptions about smoothness and structure. In robotics and AI, GPs are widely used for learning robot dynamics models, optimizing gaits, planning collision-free trajectories, environmental monitoring with mobile robots, safe reinforcement learning, and shape estimation for grasping. Their ability to quantify prediction uncertainty makes them especially valuable when data is scarce or when safety constraints must be respected — a GP can signal when it is operating outside reliably learned regions. GPs matter because they offer a mathematically rigorous way to learn from limited, noisy data while remaining honest about what is unknown. This uncertainty awareness enables safer exploration, better-informed decision-making, and tighter integration of learned models into control pipelines, making GPs a foundational tool for data-efficient, reliable robot learning.
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