Robust disturbance rejection remains a longstanding challenge in humanoid locomotion, particularly on unstructured terrains where sensing is unreliable and model mismatch is pronounced. While perception information, such as height map, enhances terrain awareness, sensor noise and sim-to-real gaps can destabilize policies in practice. In this work, we provide theoretical analysis that bounds the return gap under observation noise, when the induced latent dynamics are contractive. Furthermore, we present Contractive Mapping for Robustness (CMR) framework that maps high-dimensional, disturbance-prone observations into a latent space, where local perturbations are attenuated over time. Specifically, this approach couples contrastive representation learning with Lipschitz regularization to preserve task-relevant geometry while explicitly controlling sensitivity. Notably, the formulation can be incorporated into modern deep reinforcement learning pipelines as an auxiliary loss term with minimal additional technical effort required. Further, our extensive humanoid experiments show that CMR potently outperforms other locomotion algorithms under increased noise.

翻译：仿人机器人的鲁棒抗干扰能力始终是其运动控制领域的长期挑战，尤其在非结构化地形上，感知信息不可靠且模型失配问题显著。尽管高度图等感知信息增强了地形认知能力，但传感器噪声与仿真到现实的差距在实践中仍可能导致策略失稳。本工作提供了理论分析，证明了当诱导出的潜在动力学具有压缩性时，观测噪声下的回报差距存在上界。进一步，我们提出了鲁棒性压缩映射框架，该框架将高维、易受干扰的观测映射到一个潜在空间，其中局部扰动会随时间衰减。具体而言，该方法将对比表征学习与Lipschitz正则化相结合，在显式控制敏感度的同时保持任务相关的几何结构。值得注意的是，该框架可作为辅助损失项无缝集成到现代深度强化学习流程中，仅需极少额外技术投入。此外，我们大量的仿人机器人实验表明，在噪声增强的情况下，CMR显著优于其他运动算法。