Curriculum-Enhanced Reinforcement Learning for Robust Humanoid Locomotion

The control of humanoid locomotion remains one of the most formidable challenges in robotics. Conventional model-based approaches not only rely extensively on manual design but also exhibit limited generalization across diverse tasks and environments. To overcome these limitations, a curriculum-enhanced reinforcement learning framework is proposed in this work for training robust locomotion. Given the strong temporal dependencies inherent in humanoid locomotion, Mamba-2 is employed as the backbone of the Actor-Critic network, enabling efficient temporal modeling of historical observations and content-based reasoning with linear spatiotemporal complexity. Furthermore, to eliminate the tendency of humanoid robots to favor low-speed motion in order to maintain gait stability and balance, which often leads to inaccurate velocity command tracking, a command-guided curriculum learning (CGCL) method is proposed to improve responsiveness to velocity commands. Experimental results demonstrate that the proposed Mamba-based framework achieves state-of-the-art (SOTA) performance, while CGCL significantly enhances the accuracy of velocity command tracking. Moreover, sim-to-real transfer experiments confirm both the robustness and the seamless deployability of the proposed framework on physical humanoid platforms.