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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 structure over the detectedsegments for regularizing feature representations in the target domain.Crucially, this loss structure and network architecture can handle overlappingsegments and oversegmentation as produced by Segment Anything. We demonstratethe advantage of our method on the challenging YCB-Video and HomebrewedDBdatasets and show that it outperforms prior work and, on YCB-Video, even anetwork trained with real annotations.

Learning from SAM: Harnessing a Segmentation Foundation Model for Sim2Real Domain Adaptation through Regularization