Humanoid robots are promising to learn a diverse set of human-like locomotion behaviors, including standing up, walking, running, and jumping. However, existing methods predominantly require training independent policies for each skill, yielding behavior-specific controllers that exhibit limited generalization and brittle performance when deployed on irregular terrains and in diverse situations. To address this challenge, we propose Adaptive Humanoid Control (AHC) that adopts a two-stage framework to learn an adaptive humanoid locomotion controller across different skills and terrains. Specifically, we first train several primary locomotion policies and perform a multi-behavior distillation process to obtain a basic multi-behavior controller, facilitating adaptive behavior switching based on the environment. Then, we perform reinforced fine-tuning by collecting online feedback in performing adaptive behaviors on more diverse terrains, enhancing terrain adaptability for the controller. We conduct experiments in both simulation and real-world experiments in Unitree G1 robots. The results show that our method exhibits strong adaptability across various situations and terrains. Project website: https://ahc-humanoid.github.io.

翻译：人形机器人有望学习多种类人运动行为，包括站立、行走、奔跑和跳跃。然而，现有方法主要需要为每个技能训练独立策略，导致行为专用控制器在部署于不规则地形和多样化场景时泛化能力有限且性能脆弱。为应对这一挑战，我们提出自适应人形控制（AHC），采用两阶段框架学习跨不同技能与地形的自适应人形运动控制器。具体而言，我们首先训练若干基础运动策略，并通过多行为蒸馏过程获得基础多行为控制器，实现基于环境状态的自适应行为切换。随后，通过在更多样化地形上执行自适应行为时收集在线反馈进行强化微调，以增强控制器的地形适应能力。我们在仿真和Unitree G1机器人实体实验中进行了验证。结果表明，我们的方法在多种场景与地形中均表现出强大的适应能力。项目网站：https://ahc-humanoid.github.io。