Visuotactile sensors can provide rich contact information, having great potential in contact-rich assembly tasks. Sim2Real technique tackles the challenge of RL’s reliance on a large amount of interaction data. In this chapter, we build a general-purpose Sim2Real protocol for assembly policy learning with marker-based visuotactile sensors. To improve the simulation fidelity, we employ a FEM-based physics simulator that can simulate the sensor deformation accurately and stably for arbitrary geometries. We further propose a novel tactile feature extraction network that directly processes the set of pixel coordinates of tactile sensor markers and a self-supervised pre-training strategy to improve the efficiency and generalizability of RL policies. We conduct extensive Sim2Real experiments on the PiH task to validate the effectiveness of our method.