Survey on safe robot control via learning
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866929656949637120 |
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| author | Mabsout, Bassel El |
| author_facet | Mabsout, Bassel El |
| contents | Control systems are critical to modern technological infrastructure, spanning industries from aerospace to healthcare. This survey explores the landscape of safe robot learning, investigating methods that balance high-performance control with rigorous safety constraints. By examining classical control techniques, learning-based approaches, and embedded system design, the research seeks to understand how robotic systems can be developed to prevent hazardous states while maintaining optimal performance across complex operational environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01432 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Survey on safe robot control via learning Mabsout, Bassel El Robotics Artificial Intelligence Control systems are critical to modern technological infrastructure, spanning industries from aerospace to healthcare. This survey explores the landscape of safe robot learning, investigating methods that balance high-performance control with rigorous safety constraints. By examining classical control techniques, learning-based approaches, and embedded system design, the research seeks to understand how robotic systems can be developed to prevent hazardous states while maintaining optimal performance across complex operational environments. |
| title | Survey on safe robot control via learning |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2501.01432 |