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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2409.18327 |
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| _version_ | 1866910622638145536 |
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| author | Zhang, Jianghan Jordana, Armand Righetti, Ludovic |
| author_facet | Zhang, Jianghan Jordana, Armand Righetti, Ludovic |
| contents | The recent promises of Model Predictive Control in robotics have motivated the development of tailored second-order methods to solve optimal control problems efficiently. While those methods benefit from strong convergence properties, tailored efficient implementations are challenging to derive. In this work, we study the potential effectiveness of first-order methods and show on a torque controlled manipulator that they can equal the performances of second-order methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18327 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerated gradient descent for high frequency Model Predictive Control Zhang, Jianghan Jordana, Armand Righetti, Ludovic Robotics The recent promises of Model Predictive Control in robotics have motivated the development of tailored second-order methods to solve optimal control problems efficiently. While those methods benefit from strong convergence properties, tailored efficient implementations are challenging to derive. In this work, we study the potential effectiveness of first-order methods and show on a torque controlled manipulator that they can equal the performances of second-order methods. |
| title | Accelerated gradient descent for high frequency Model Predictive Control |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.18327 |