An Introduction to Zero-Order Optimization Techniques for Robotics
The paper introduces random search as a unifying mathematical framework for zero-order optimization techniques used in robotics, such as trajectory optimization and reinforcement learning. It explains how sampling-based methods, including various gradient approximation techniques and stochastic dynamics like Simulated Annealing, can effectively handle non-differentiable functions and escape local minima.