用强化学习优化蚊幼虫游动模式，提升仿生机器人的运动效率。
Fine Tuning Swimming Locomotion Learned from Mosquito Larvae
- 通过强化学习微调蚊幼虫游动参数，实现局部优化。
- 改进后游动效率显著提升，验证了方法有效性。
- 适合对生物启发式机器人与流体动力学优化感兴趣的读者。
此前研究分析了蚊幼虫的后向游动行为，进行了参数化并复现于计算流体动力学（CFD）模型中。由于该游动模式源自真实观察，未必是模型游泳器的最优运动方式。本项目进一步优化此复制方案：利用强化学习指导局部参数更新。鉴于CFD模型计算成本高，额外训练深度学习模型以模拟作用于游泳器的受力。结果表明，该方法能有效进行局部搜索，改善参数化游动模式。
原文摘要 · Abstract (English)
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.
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