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This tutorial reframes random search as a unifying lens for zero-order methods used in robotics, and uses that lens to group many trajectory optimization algorithms and to derive new reinforcement learning methods. For ML readers, the practical value is the claim that derivative-free search can match gradient-based policy optimization in some settings, especially with non-differentiable objectives or local minima. The authors' "competitive" performance claims for the new RL algorithms would need to be checked against the benchmarks in the paper.
An Introduction to Zero-Order Optimization Techniques for Robotics
arXiv:2506.22087v3 Announce Type: replace 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.
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.
Robotics (cs.RO)
arXiv:2506.22087 [cs.RO]
(or arXiv:2506.22087v3 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2506.22087 Focus to learn more arXiv-issued DOI via DataCite
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