The main challenge of pose estimation for six degrees of freedom (6DoF) is the lack of labeled data in real environment. In order to overcome this problem, many studies recently have trained deep learning models with synthetic data. However, a domain gap between real and synthetic environments exists, prompting various approaches to address this issue. In this work, we propose domain adaptation for self-supervised 6DoF pose estimation (DAPO), which leverages the components and introduces an effective method to reduce domain discrepancy. First, we adopt a multi-level domain adaptation module, on image level and instance level, to learn domain-invariant features. Second, we used entropy-based alignment to minimize the entropy of representation embedding. Finally, we evaluate our approach on LineMOD and Occlusion-LineMOD datasets. Experiments show that our proposed method achieves higher performance compared to the prior methods and demonstrate effectiveness in domain shift scenarios on 6DoF pose estimation.