Dynamics Modeling of Soft Robots Based on Attention-enhanced Lagrangian Deep Neural Networks
Yeqi Wei, Xiangyu Shao, Jingyue Liu, Shaojie Zhang, Linke Xu · 2024
This study explores a method for the dynamic modeling of soft robots, focusing on enhancing the deep learning-based Lagrangian modeling approach through the attention mechanism, which enriches the training process by allocating focused attention and analytical weighting to critical state features, thereby increasing the model’s sensitivity to changes in the robot’s state. We compared our method through simulation, demonstrating that the model is effective in long-term prediction and noise rejection.