Closing the Control Loop with Time-Variant Embedded Soft Sensors and Recurrent Neural Networks.

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 perio

SUBMITTER: George Thuruthel T 

PROVIDER: S-EPMC9805858 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature