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Data-driven Methods Applied to Soft Robot Modeling and Control: A Review

Data-driven Methods Applied to Soft Robot Modeling and Control: A Review

20 May 2023
Zixiao Chen
F. Renda
A. L. Gall
Lorenzo Mocellin
Matteo Bernabei
Théo Dangel
G. Ciuti
M. Cianchetti
Cesare Stefanini
ArXivPDFHTML

Papers citing "Data-driven Methods Applied to Soft Robot Modeling and Control: A Review"

4 / 4 papers shown
Title
Learning Low-Dimensional Strain Models of Soft Robots by Looking at the Evolution of Their Shape with Application to Model-Based Control
Learning Low-Dimensional Strain Models of Soft Robots by Looking at the Evolution of Their Shape with Application to Model-Based Control
Ricardo Valadas
Maximilian Stolzle
Jingyue Liu
Cosimo Della Santina
70
1
0
21 Feb 2025
Multi-Agent Reinforcement Learning for Connected and Automated Vehicles
  Control: Recent Advancements and Future Prospects
Multi-Agent Reinforcement Learning for Connected and Automated Vehicles Control: Recent Advancements and Future Prospects
Min Hua
Dong Chen
Xinda Qi
Kun Jiang
Z. Liu
Quan Zhou
Hongming Xu
28
10
0
18 Dec 2023
Toward Zero-Shot Sim-to-Real Transfer Learning for Pneumatic Soft Robot
  3D Proprioceptive Sensing
Toward Zero-Shot Sim-to-Real Transfer Learning for Pneumatic Soft Robot 3D Proprioceptive Sensing
Uksang Yoo
Hanwen Zhao
A. Altamirano
Wenzhen Yuan
Chen Feng
3DPC
43
13
0
08 Mar 2023
A MATLAB Toolbox for Hybrid Rigid Soft Robots Based on the Geometric
  Variable Strain Approach
A MATLAB Toolbox for Hybrid Rigid Soft Robots Based on the Geometric Variable Strain Approach
A. Mathew
Ikhlas Mohamed Ben Hmida
C. Armanini
F. Boyer
F. Renda
86
50
0
12 Jul 2021
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