  Real-world robotics problems often occur in domains that differ significantly
from the robot's prior training environment. For many robotic control tasks,
real world experience is expensive to obtain, but data is easy to collect in
either an instrumented environment or in simulation. We propose a novel domain
adaptation approach for robot perception that adapts visual representations
learned on a large easy-to-obtain source dataset (e.g. synthetic images) to a
target real-world domain, without requiring expensive manual data annotation of
real world data before policy search. Supervised domain adaptation methods
minimize cross-domain differences using pairs of aligned images that contain
the same object or scene in both the source and target domains, thus learning a
domain-invariant representation. However, they require manual alignment of such
image pairs. Fully unsupervised adaptation methods rely on minimizing the
discrepancy between the feature distributions across domains. We propose a
novel, more powerful combination of both distribution and pairwise image
alignment, and remove the requirement for expensive annotation by using weakly
aligned pairs of images in the source and target domains. Focusing on adapting
from simulation to real world data using a PR2 robot, we evaluate our approach
on a manipulation task and show that by using weakly paired images, our method
compensates for domain shift more effectively than previous techniques,
enabling better robot performance in the real world.
