Robot Learning

Bayesian Optimization

Bayesian optimization is a sample-efficient method for optimizing expensive black-box functions by fitting a probabilistic surrogate, typically a Gaussian process, to observed evaluations and choosing the next query point via an acquisition function that balances exploration and exploitation. In robotics it is used to tune controller gains, hardware morphology, and simulation parameters directly from physical trials, where each evaluation costs real robot time.

Why it matters for physical AI

When each experiment consumes minutes of robot wear and human supervision, extracting the most from a handful of trials matters, and Bayesian optimization remains the standard tool for hardware-in-the-loop tuning.