Robot Learning

Zeroth-Order Optimization

Zeroth-order optimization is optimization using only function evaluations, without analytic gradients, estimating search directions from sampled perturbations as in evolution strategies, CMA-ES, and random search. It suits robotics problems where gradients are unavailable or unreliable, including hardware-in-the-loop tuning, controllers evaluated through contact-rich simulation, and policy search, where OpenAI's evolution strategies work showed it can rival gradient-based reinforcement learning.

Why it matters for physical AI

Contact discontinuities and black-box simulators break backpropagation, so gradient-free methods remain a dependable fallback for tuning gaits, reward weights, and sim-to-real parameters directly against task outcomes.