Fine Tuning Swimming Locomotion Learned from Mosquito Larvae
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866908707727605760 |
|---|---|
| author | Rajbhandari, Pranav Dhileep, Karthick Ravi, Sridhar Sofge, Donald |
| author_facet | Rajbhandari, Pranav Dhileep, Karthick Ravi, Sridhar Sofge, Donald |
| contents | In prior research, we analyzed the backwards swimming motion of mosquito larvae, parameterized it, and replicated it in a Computational Fluid Dynamics (CFD) model. Since the parameterized swimming motion is copied from observed larvae, it is not necessarily the most efficient locomotion for the model of the swimmer. In this project, we further optimize this copied solution for the swimmer model. We utilize Reinforcement Learning to guide local parameter updates. Since the majority of the computation cost arises from the CFD model, we additionally train a deep learning model to replicate the forces acting on the swimmer model. We find that this method is effective at performing local search to improve the parameterized swimming locomotion. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02702 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fine Tuning Swimming Locomotion Learned from Mosquito Larvae Rajbhandari, Pranav Dhileep, Karthick Ravi, Sridhar Sofge, Donald Neural and Evolutionary Computing Artificial Intelligence In prior research, we analyzed the backwards swimming motion of mosquito larvae, parameterized it, and replicated it in a Computational Fluid Dynamics (CFD) model. Since the parameterized swimming motion is copied from observed larvae, it is not necessarily the most efficient locomotion for the model of the swimmer. In this project, we further optimize this copied solution for the swimmer model. We utilize Reinforcement Learning to guide local parameter updates. Since the majority of the computation cost arises from the CFD model, we additionally train a deep learning model to replicate the forces acting on the swimmer model. We find that this method is effective at performing local search to improve the parameterized swimming locomotion. |
| title | Fine Tuning Swimming Locomotion Learned from Mosquito Larvae |
| topic | Neural and Evolutionary Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2412.02702 |