3 accepted papers
This letter proposes an adaptive radial basis function neural network (RBF NN) based scheme for the dynamics learning and tracking control problems of a soft trunk robot. Specifically, a low-order approximate model describing the soft robot's dynamics is first derived with the finite element method
This letter presents a radial basis function neural network (RBF NN) based methodology to investigate the dynamics modeling and fault detection (FD) problems for soft robots. Finite element method (FEM) is first used to derive a mathematical model to describe the dynamics of a soft trunk robot. An a
Human can infer the 3D geometry of a scene from a sketch instead of a realistic image, which indicates that the spatial structure plays a fundamental role in understanding the depth of scenes. We are the first to explore the learning of a depth-specific structural representation, which captures the…