# An Introduction to Zero-Order Optimization Techniques for Robotics

arXiv: https://arxiv.org/abs/2506.22087 · HTML: https://mlsanity.com/paper/2506.22087/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2025.

## Built on by (most cited first, 4 of 4)

- [You've Got a Golden Ticket: Improving Generative Robot Policies With A Single Noise Vector](https://mlsanity.com/paper/2603.15757/) (2026)
- [Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation](https://mlsanity.com/paper/2602.08557/) (2026)
- [Coupled Local and Global World Models for Efficient First Order RL](https://mlsanity.com/paper/2602.06219/) (2026)
- [VAMPS: Visual and Motor Policies from Sampling-Based Planning](https://mlsanity.com/paper/2610.05331/) (2026)

## Abstract (arXiv)

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.