Semi-supervised Grasp Detection by Representation Learning in a Vector Quantized Latent Space
share
Determining quality grasps from an image is an important area of research. In this work, we present a semi-supervised learning based grasp detection approach which models a discrete latent space using a Vector Quantized Variational Autoencoder (VQ-VAE). To the best of our knowledge, this is the first time VAEs have been applied in the domain of robot grasp detection. The VAE helps the model in generalizing beyond the Cornell Grasping Dataset (CGD) despite having limited amount of labelled data. We validate this claim by testing the model on images not there in the CGD. Also, the model performs significantly better than existing approaches which do not make use of unlabeled images to improve the grasp.
READ FULL TEXT