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

Abstract

Soft robots show compliance and have infinite degrees of freedom. Thanks to these properties, such robots can be leveraged for surgery, rehabilitation, biomimetics, unstructured environment exploring, and industrial grippers. In this case, they attract scholars from a variety of areas. However, nonlinearity and hysteresis effects also bring a burden to robot modeling. Moreover, following their flexibility and adaptation, soft robot control is more challenging than rigid robot control. In order to model and control soft robots, a large number of data-driven methods are utilized in pairs or separately. This review first briefly introduces two foundations for data-driven approaches, which are physical models and the Jacobian matrix, then summarizes three kinds of data-driven approaches, which are statistical method, neural network, and reinforcement learning. This review compares the modeling and controller features, e.g., model dynamics, data requirement, and target task, within and among these categories. Finally, we summarize the features of each method. A discussion about the advantages and limitations of the existing modeling and control approaches is presented, and we forecast the future of data-driven approaches in soft robots. A website (https://sites.google.com/view/23zcb) is built for this review and will be updated frequently.

arXiv comment16 pages, 6 figures, 7tables, accepted by IEEE Transactions on Automation Science and Engineering on 11 March, 2024

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Cited by in corpus (4)

• Learning-based Nonlinear Model Predictive Control of Articulated Soft Robots using Recurrent Neural Networks
• A Hybrid Adaptive Controller for Soft Robot Interchangeability
• A Versatile Neural Network Configuration Space Planning and Control Strategy for Modular Soft Robot Arms
• Payload generalization on feedforward control actions for trajectory tracking in robots with rigid and elastic joints