Whole-body humanoid motion represents a fundamental challenge in robotics, requiring balance, coordination, and adaptability to enable human-like behaviors. However, existing methods typically require multiple training samples per motion, rendering the collection of high-quality human motion datasets both labor-intensive and costly. To address this, we propose a data-efficient adaptation approach that learns a new humanoid motion from a single non-walking target sample together with auxiliary walking motions and a walking-trained base model. The core idea lies in leveraging order-preserving optimal transport to compute distances between walking and non-walking sequences, followed by interpolation along geodesics to generate new intermediate pose skeletons, which are then optimized for collision-free configurations and retargeted to the humanoid before integration into a simulated environment for policy adaptation via reinforcement learning. Experimental evaluations on the CMU MoCap dataset demonstrate that our method consistently outperforms baselines, achieving superior performance across metrics. Our code is available at: https://github.com/hhuang-code/One-shot-WBM.

翻译：人形全身运动是机器人领域的一项基础性挑战，需要平衡、协调和适应性才能实现类人行为。然而，现有方法通常需要每个运动有多个训练样本，这使得高质量人体运动数据集的采集既费时又费力。为了解决这个问题，我们提出了一种数据高效的适应方法，该方法从单个非行走目标样本以及辅助行走运动和基于行走训练的基模型学习新的人形运动。其核心思想在于利用保序最优传输来计算行走与非行走序列之间的距离，随后沿测地线进行插值以生成新的中间姿态骨架，然后对这些骨架进行优化以实现无碰撞构型，并将其重定向到人形，最后集成到模拟环境中，通过强化学习进行策略适应。在CMU MoCap数据集上的实验评估表明，我们的方法始终优于基线方法，在各项指标上均取得了卓越性能。我们的代码可在以下网址获取：https://github.com/hhuang-code/One-shot-WBM。