Physics-Informed Recurrent Neural Networks for Soft Pneumatic Actuators

Replacing sensors with indirect sensing techniques contributes to retaining the flexibility of soft robots. By combining physical models with recurrent neural networks (which we term a physics-informed recurrent neural network [PIRNN] approach), we implemented a hybrid prediction scheme on two typic...

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Veröffentlicht in:	IEEE robotics and automation letters 2022-07, Vol.7 (3), p.6862-6869
Hauptverfasser:	Sun, Wentao, Akashi, Nozomi, Kuniyoshi, Yasuo, Nakajima, Kohei
Format:	Artikel
Sprache:	eng
Schlagworte:	Actuators and learning for soft robots Artificial muscles Computational modeling control Machine learning Mathematical models modeling Pneumatic actuators Predictive models Recurrent neural networks Sensors Soft robot applications Soft robotics soft sensors and actuators
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