2 papers
cs.RO2025
Learn to Swim: Data-Driven LSTM Hydrodynamic Model for Quadruped Robot Gait Optimization
Fei Han, Pengming Guo, Hao Chen +5
This paper presents a Long Short-Term Memory network-based Fluid Experiment Data-Driven model (FED-LSTM) for predicting unsteady, nonlinear hydrodynamic forces on the underwater qu…
physics.flu-dyn2023
Learn to Flap: Foil Non-parametric Path Planning via Deep Reinforcement Learning
Z. P. Wang, R. J. Lin, Z. Y. Zhao +3
To optimize flapping foil performance, the application of deep reinforcement learning (DRL) on controlling foil non-parametric motion is conducted in the present study. Traditional…