  Robotic Perception in diverse domains such as low-light scenarios, where new
modalities like thermal imaging and specialized night-vision sensors are
increasingly employed, remains a challenge. Largely, this is due to the limited
availability of labeled data. Existing Domain Adaptation (DA) techniques, while
promising to leverage labels from existing well-lit RGB images, fail to
consider the characteristics of the source domain itself. We holistically
account for this factor by proposing Source Preparation (SP), a method to
mitigate source domain biases. Our Almost Unsupervised Domain Adaptation (AUDA)
framework, a label-efficient semi-supervised approach for robotic scenarios --
employs Source Preparation (SP), Unsupervised Domain Adaptation (UDA) and
Supervised Alignment (SA) from limited labeled data. We introduce
CityIntensified, a novel dataset comprising temporally aligned image pairs
captured from a high-sensitivity camera and an intensifier camera for semantic
segmentation and object detection in low-light settings. We demonstrate the
effectiveness of our method in semantic segmentation, with experiments showing
that SP enhances UDA across a range of visual domains, with improvements up to
40.64% in mIoU over baseline, while making target models more robust to
real-world shifts within the target domain. We show that AUDA is a
label-efficient framework for effective DA, significantly improving target
domain performance with only tens of labeled samples from the target domain.
