Tendon-driven bipedal robots exhibit complex time-varying dynamics due to elastic deformations, multi-joint coupling, and transmission delays. These characteristics lead to significant sim-to-real discrepancies and limit the robot’s performance in complex terrains. To address this issue, we propose a two-stage locomotion control training framework based on Real2Sim2Real (RSR). In the first stage, joint motion data collected from the real robot are used to train a torque refinement policy in simulation, implicitly modeling the time-varying dynamics of the tendon-driven system and reducing the body dynamics gap during sim-to-real transfer. In the second stage, we introduce a reinforcement learning approach that integrates explicit estimation with implicit representation. By explicitly estimating body linear velocity and local terrain information under the feet, and simultaneously learning task-relevant latent features through implicit representation, the robot’s adaptability to complex terrains is enhanced. Experimental results show that, for a forward velocity tracking task of 2.5 m/s, the proposed explicit–implicit learning method achieves a 15.9% reduction in velocity tracking error compared to the purely implicit representation baseline (IWM). When further combined with the torque refinement policy (RSR), the tracking error is further reduced by 86.4% compared to the explicit–implicit baseline (EIWM). Moreover, the proposed method enables stable locomotion across various complex terrains, demonstrating its effectiveness in improving sim-to-real transfer performance and terrain adaptability.