Zero-order optimzation

Currently, I am interested in the use of gradient-free techniques to solve complex motion planning problems. I recently wrote a tutorial paper on the links between several popular algorithms used in robotics. During my postdoc at LAAS, I aim to further investigate this topic. I am especially interested in how we can obtain a diversity of high-quality solutions for a given problem.

Selected publication

• Armand Jordana, Jianghan Zhang, Joseph Amigo, and Ludovic Righetti. “An Introduction to Zero-Order Optimization Techniques for Robotics.” preprint (2025).

  Paper

  Abstract

  Zero-order optimization techniques are becoming increasingly popular in robotics due to their ability to handle non-differentiable functions and escape local minima. These advantages make them particularly useful for trajectory optimization and policy optimization. In this work, we propose a mathematical tutorial on random search. It offers a simple and unifying perspective for understanding a wide range of algorithms commonly used in robotics. Leveraging this viewpoint, we classify many trajectory optimization methods under a common framework and derive novel competitive RL algorithms.