Domain　adaptation　is　an　important　property　in　robot　vision,　which　enables　the　neural　networks　pre-trained　on　source　domains　to　adapt　target　domains　automatically　without　any　annotation　efforts.　During　this　process,　source　data　is　not　always　accessible　due　to　the　constraints　of　expensive　storage　overhead　and　data　privacy　protection.　Therefore,　the　source　domain　pre-trained　model　is　expected　to　optimize　with　only　unlabeled　target　data,　termed　as　source-free　unsupervised　domain　adaptation.　In　this　paper,　we　view　this　problem　as　a　special　case　of　noisy　label　learning,　since　the　given　pre-trained　model　can　generate　noisy　labels　for　unlabeled　target　data　via　network　inference.　The　potential　semantic　cues　for　unsupervised　domain　adaptation　exactly　lie　on　these　noisy　labels.　Inspired　by　this　problem　modeling,　we　propose　a　simple　yet　effective　Self-Supervised　Noisy　Label　Learning　method,　which　injects　self-supervised　learning　to　impose　the　intrinsic　data　structure　and　facilitate　label-denoising.　Extensive　experiments　have　been　conducted　on　diverse　benchmarks　to　validate　the　effectiveness.　Our　method　achieves　state-of-the-art　performance.