CMR：用于在无结构地形上稳健人形运动的收缩映射嵌入
CMR: Contractive Mapping Embeddings for Robust Humanoid Locomotion on Unstructured Terrains
AI总结：
CMR通过结合对比表征学习与Lipschitz正则化，提升人形机器人在无结构地形中抗扰动的运动能力。
摘要：
稳健的扰动拒绝仍然是人形运动中的长期挑战，尤其是在无结构地形上，传感不可靠且模型不匹配现象显著。虽然感知信息，如高度图，可以增强地形意识，但传感器噪声和仿真到现实的差距可能导致策略在实践中不稳定。在本工作中，我们提供了理论分析，界定了在观察噪声下返回差距，当诱导的潜在动态是收缩性时。此外，我们提出了用于稳健性的收缩映射（CMR）框架，将高维、易受干扰的观测映射到潜在空间，在该空间中局部扰动随时间衰减。具体而言，该方法将对比表征学习与Lipschitz正则化相结合，以保持任务相关几何结构，同时显式控制敏感性。值得注意的是，该方法可以作为现代深度强化学习流程中的辅助损失项，仅需最小的额外技术努力即可整合。进一步，我们的广泛人形实验表明，CMR在增加噪声下显著优于其他运动算法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
