CMR: 収縮写像埋め込みによる不整地でのロバストなヒューマノイド歩行
CMR: Contractive Mapping Embeddings for Robust Humanoid Locomotion on Unstructured Terrains
観測ノイズに強い潜在表現を学習する収縮写像埋め込み(CMR)を提案し、不整地でのヒューマノイド歩行をロバスト化した研究。
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著者: Qixin Zeng, Hongyin Zhang, Shangke Lyu, Junxi Jin, Donglin Wang, Chao Huang
分類: cs.RO, cs.AI
原文アブストラクト
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.
