Meta-Learning-Based Optimal Control for Soft Robotic Manipulators to Interact with Unknown Environments

Author:

Tang Zhiqiang1,Wang Peiyi2,Xin Wenci1,Xie Zhexin1,Kan Longxin1,Mohanakrishnan Muralidharan1,Laschi Cecilia1

Affiliation:

1. National University of Singapore,Department of Mechanical Engineering,Singapore

2. Robotics Research Center, Beijing Jiaotong University,Beijing,China

Funder

National Research Foundation, Singapore

Publisher

IEEE

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Nonlinear Modeling for Soft Pneumatic Actuators via Data-Driven Parameter Estimation;2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM);2024-07-15

2. Pushing with Soft Robotic Arms via Deep Reinforcement Learning;Advanced Intelligent Systems;2024-07-08

3. Optimized Design of a Soft Actuator Considering Force/Torque, Bendability, and Controllability via an Approximated Structure;Journal of Mechanical Design;2024-06-03

4. Torque-Bounded Admittance Control With Implicit Euler Realization of Set-Valued Operators;IEEE/ASME Transactions on Mechatronics;2024

5. Data-Driven Methods Applied to Soft Robot Modeling and Control: A Review;IEEE Transactions on Automation Science and Engineering;2024

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