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Domain adaptation is especially important for robotics applications, wheretarget domain training data is usually scarce and annotations are costly toobtain. We present a method for self-supervised domain adaptation for thescenario where annotated source domain data (e.g. from synthetic generation) isavailable, but the target domain data is completely unannotated. Our methodtargets the semantic segmentation task and leverages a segmentation foundationmodel (Segment Anything Model) to obtain segment information on unannotateddata. We take inspiration from recent advances in unsupervised local featurelearning and propose an invariance-variance loss over the detected segments forregularizing feature representations in the target domain. Crucially, this lossstructure and network architecture can handle overlapping segments andoversegmentation as produced by Segment Anything. We demonstrate the advantageof our method on the challenging YCB-Video and HomebrewedDB datasets and showthat it outperforms prior work and, on YCB-Video, even a network trained withreal annotations. Additionally, we provide insight through model ablations andshow applicability to a custom robotic application.

Learning from SAM: Harnessing a Foundation Model for Sim2Real Adaptation by Regularization