  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 simulators, and their representation from visual
inputs may transfer well to the real world. In particular, learning methods
based on noise contrastive estimation have shown promising results. To
robustify the simulation-to-real transfer, domain randomization (DR) was
suggested for learning features that are invariant to irrelevant visual
properties such as textures or lighting. In this work, however, we show that a
naive application of DR to unsupervised learning based on contrastive
estimation does not promote invariance, as the loss function maximizes mutual
information between the features and both the relevant and irrelevant visual
properties. We propose a simple modification of the contrastive loss to fix
this, exploiting the fact that we can control the simulated randomization of
visual properties. Our approach learns physical features that are significantly
more robust to visual domain variation, as we demonstrate using both rigid and
non-rigid objects.
