Convolutional‐Generative Adversarial Network: Data‐Driven Mechanical Inverse Method for Intelligent Tactile Perception

Abstract

Inverse problem method uses the results of observations to infer the model parameters of a given system, which can be used in the area of tactile perception. By integrating tactile perception in robotic systems, reconstructing structural parameters of target object can be achieved. However, with insufficient information, how to evaluate complex structural parameters accurately remains a challenge. A data‐driven method is proposed for the structural perception based on convolutional‐generative adversarial network (CGAN), which can precisely evaluate the structural parameters, notwithstanding missing a large quantity of sampled strains randomly in space domain. The CGAN model has been verified on a reconfigurable structure. Both the numerical calculations and experiments indicate that the structural accuracy rate can reach above 90% in spite of the strain loss ratio being 50%. Through inpainting the observations and discretizing the model parameters, a complete process is proposed to deal with the inverse problem of predicting continuous structure from the incomplete strain, which provides a new solution for applying machine learning method into intelligence tactile robot.