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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2506.22087 |
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| _version_ | 1866908585420652544 |
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| author | Jordana, Armand Zhang, Jianghan Amigo, Joseph Righetti, Ludovic |
| author_facet | Jordana, Armand Zhang, Jianghan Amigo, Joseph Righetti, Ludovic |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22087 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Introduction to Zero-Order Optimization Techniques for Robotics Jordana, Armand Zhang, Jianghan Amigo, Joseph Righetti, Ludovic Robotics 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. |
| title | An Introduction to Zero-Order Optimization Techniques for Robotics |
| topic | Robotics |
| url | https://arxiv.org/abs/2506.22087 |