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• W4224254803 abstract "Embedded soft sensors can significantly impact the design and control of soft-bodied robots. Although there have been considerable advances in technology behind these novel sensing materials, their application in real-world tasks, especially in closed-loop control tasks, has been severely limited. This is mainly because of the challenge involved with modeling a nonlinear time-variant sensor embedded in a complex soft-bodied system. This article presents a learning-based approach for closed-loop force control with embedded soft sensors and recurrent neural networks (RNNs). We present learning protocols for training a class of RNNs called long short-term memory (LSTM) that allows us to develop accurate and robust state estimation models of these complex dynamical systems within a short period of time. Using this model, we develop a simple feedback force controller for a soft anthropomorphic finger even with significant drift and hysteresis in our feedback signal. Simulation and experimental studies are conducted to analyze the capabilities and generalizability of the control architecture. Experimentally, we are able to develop a closed-loop controller with a control frequency of 25 Hz and an average accuracy of 0.17 N. Our results indicate that current soft sensing technologies can already be used in real-world applications with the aid of machine learning techniques and an appropriate training methodology." @default.
• W4224254803 created "2022-04-26" @default.
• W4224254803 creator A5037510570 @default.
• W4224254803 creator A5041874420 @default.
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• W4224254803 date "2022-12-01" @default.
• W4224254803 modified "2023-10-18" @default.
• W4224254803 title "Closing the Control Loop with Time-Variant Embedded Soft Sensors and Recurrent Neural Networks" @default.
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• W4224254803 doi "https://doi.org/10.1089/soro.2021.0012" @default.
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