Dynamics Modeling of Soft Robots Based on Attention-enhanced Lagrangian Deep Neural Networks

Conference paper (2024)

Authors

Yeqi Wei Harbin Institute of Technology
X. Shao Harbin Institute of Technology
J. Liu Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Shaojie Zhang Harbin Institute of Technology
Linke Xu Harbin Institute of Technology
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
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Published Date

2024

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

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.

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