Perceptive bipedal locomotion over sparse terrain remains a difficult challenge: model-based methods are precise but brittle to uncertainty, while model-free methods are robust but struggle to discover the precise, constrained motions required for safety-critical locomotion where small errors can cause catastrophic failures. We propose a model-assisted reinforcement learning (RL) framework that combines both perspectives in three steps: (1) generate a safe reference trajectory using simplified models; (2) train a privileged teacher policy guided by a control Lyapunov function (CLF) reward built around the safe reference trajectory; and (3) distill the teacher into a vision-based student policy. We show that this model-assistance procedure produces physically grounded locomotion, improving sample efficiency, reducing the need for a complex learning curriculum, and achieving smoother locomotion behavior alongside stepping stone performance comparable to model-free baselines. We validate our approach in simulation and demonstrate successful deployment on a Unitree G1 humanoid robot navigating sparse footholds with lateral constraints.

翻译：在稀疏地形上的感知型双足运动仍是一项艰巨挑战：基于模型的方法精确但易受不确定性影响，而基于无模型的方法鲁棒性强却难以发现安全关键型运动中所需的精确约束运动——此类场景下微小误差即可导致灾难性失效。我们提出一种模型辅助强化学习框架，通过三步融合两者优势：(1) 利用简化模型生成安全参考轨迹；(2) 基于围绕该参考轨迹构建的控制李雅普诺夫函数奖励，训练特权教师策略；(3) 将教师策略蒸馏为基于视觉的学生策略。研究表明，这种模型辅助流程可产生物理根基稳固的运动，提升样本效率，降低对复杂学习课程的需求，并在获得与无模型基线相当的踏脚石性能的同时实现更平滑的运动行为。我们在仿真中验证该方法，并成功将其部署于受横向约束的Unitree G1人形机器人上，实现稀疏立足点导航。