CoRe-MoE: Contrastive Reweighted Mixture of Experts for Multi-Terrain Humanoid Locomotion with Gait Adaptation

The CoRe-MoE framework introduces a two-stage reinforcement learning approach for humanoid locomotion that effectively integrates gait adaptation and multi-terrain navigation. By decoupling gait generation from terrain adaptation, it employs a Mixture-of-Experts (MoE) architecture with a contrastive objective to enhance expert specialization and structured terrain representation. Simulation results indicate superior performance in success rate and stability, with real-world validation on a Unitree G1 robot demonstrating effective locomotion across diverse terrains, making it a significant advancement for practitioners in humanoid robotics and adaptive locomotion systems.