Fieldable low-light perception systems require both semantic and geometric scene understanding under severe domain shift and embedded compute constraints. We present FAMDA, a foundation-model-assisted self-training framework for unsupervised domain adaptation (UDA) in multi-task dense prediction. The framework integrates segmentation and depth foundation models into a self-training paradigm to generate high-quality pseudo-labels for the target domain, distilling their generalization capabilities into a single lightweight student network. To connect the method to deployment, we instantiate the adapted student on a compact handheld low-light prototype with DomiNite imaging and Jetson Orin Nano embedded compute. We evaluate on standard synthetic-to-real multi-task UDA benchmarks for comparison with prior work and on our field-collected low-light dataset to validate daytime-to-low-light adaptation in the target application setting. Across these evaluations, the proposed approach achieves strong segmentation and depth accuracy with substantially lower memory use and inference latency than direct foundation-model inference, demonstrating a practical path toward embedded multi-task perception for low-light field systems.

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