Semantic Communication and Control Co-Design for Multi-Objective Distinct Dynamics
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866918232526422016 |
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| author | Girgis, Abanoub M. Seo, Hyowoon Bennis, Mehdi |
| author_facet | Girgis, Abanoub M. Seo, Hyowoon Bennis, Mehdi |
| contents | This letter introduces a machine-learning approach to learning the semantic dynamics of correlated systems with different control rules and dynamics. By leveraging the Koopman operator in an autoencoder (AE) framework, the system's state evolution is linearized in the latent space using a dynamic semantic Koopman (DSK) model, capturing the baseline semantic dynamics. Signal temporal logic (STL) is incorporated through a logical semantic Koopman (LSK) model to encode system-specific control rules. These models form the proposed logical Koopman AE framework that reduces communication costs while improving state prediction accuracy and control performance, showing a 91.65% reduction in communication samples and significant performance gains in simulation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_02303 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Semantic Communication and Control Co-Design for Multi-Objective Distinct Dynamics Girgis, Abanoub M. Seo, Hyowoon Bennis, Mehdi Robotics Machine Learning Systems and Control This letter introduces a machine-learning approach to learning the semantic dynamics of correlated systems with different control rules and dynamics. By leveraging the Koopman operator in an autoencoder (AE) framework, the system's state evolution is linearized in the latent space using a dynamic semantic Koopman (DSK) model, capturing the baseline semantic dynamics. Signal temporal logic (STL) is incorporated through a logical semantic Koopman (LSK) model to encode system-specific control rules. These models form the proposed logical Koopman AE framework that reduces communication costs while improving state prediction accuracy and control performance, showing a 91.65% reduction in communication samples and significant performance gains in simulation. |
| title | Semantic Communication and Control Co-Design for Multi-Objective Distinct Dynamics |
| topic | Robotics Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2410.02303 |