Unsupervised Feature Learning for Manipulation with Contrastive Domain Randomization
Carmel Rabinovitz,Niko Grupen,Aviv Tamar,Carmel Rabinovitz,Niko Grupen,Aviv Tamar
Robotic tasks such as manipulation with visual inputs require image features that capture the physical properties of the scene, e.g., the position and configuration of objects. Recently, it has been suggested to learn such features in an unsupervised manner from simulated, self-supervised, robot interaction; the idea being that high-level physical properties are well captured by modern physical si...
