Contrastive Prediction and Estimation of Deformable Objects based on Improved Resnet

Abstract

Because the dynamic model of deformable linear object is complex, the learning based on visual model is difficult, and the feature information extraction is insufficient. Therefore, we propose a joint visual representation model using contrast learning of optimized encoder. We start with the encoder, add the residual structure to the encoder, optimize the extraction and compression of its feature information, and control its parameters to 3 million. In this way, we can not only obtain excellent feature information, but also have good efficiency. In the rope experiment, we collect information from the simulated environment without manual marking, extract features through the encoder and transmit them to the downstream task. Experiments show that the evaluation of our model at 135 ° and 45 ° is improved by about 50%.