  The goal of this work is to address the recent success of domain
randomization and data augmentation for the sim2real setting. We explain this
success through the lens of causal inference, positioning domain randomization
and data augmentation as interventions on the environment which encourage
invariance to irrelevant features. Such interventions include visual
perturbations that have no effect on reward and dynamics. This encourages the
learning algorithm to be robust to these types of variations and learn to
attend to the true causal mechanisms for solving the task. This connection
leads to two key findings: (1) perturbations to the environment do not have to
be realistic, but merely show variation along dimensions that also vary in the
real world, and (2) use of an explicit invariance-inducing objective improves
generalization in sim2sim and sim2real transfer settings over just data
augmentation or domain randomization alone. We demonstrate the capability of
our method by performing zero-shot transfer of a robot arm reach task on a 7DoF
Jaco arm learning from pixel observations.
