一个控制器统一实现人形机器人走、跑、摔倒恢复，无需切换模式。
Unified Walking, Running, and Recovery for Humanoids via State-Dependent Adversarial Motion Priors
- 用状态依赖门控机制，自动判断何时用行走/跑步或恢复判别器。
- 仅需3段参考动作数据，就能训练出覆盖全行为的统一策略。
- 部署时无逻辑判断，硬件实测可从正倒、侧倒中恢复并流畅变速。
我们提出一种统一的强化学习框架，使单个策略可在Unitree G1人形机器人上完成行走、跑步及跌倒恢复，且在真实硬件上部署时无需任何显式模式切换指令。该框架通过将传统的全局参考分布替换为状态依赖的门控机制，将每个训练样本路由至两个判别器之一：专用恢复判别器与速度条件化的运动判别器（联合覆盖行走与跑步）。门控由投影重力的单一固定阈值定义：当身体倾斜角超过约37°（|g_z+1|>0.6）时激活恢复判别器；否则使用运动判别器，以归一化命令速度作为条件，在步行与跑步参考轨迹间选择。仅需三段LAFAN1参考动作片段即可正则化完整行为集。部署时，一个冻结的ONNX策略以50Hz运行，无需实时模式逻辑；硬件实验表明，该控制器能成功实现俯卧与仰卧跌倒后的恢复，并在相同控制下完成平滑的走-跑过渡。
原文摘要 · Abstract (English)
We propose a unified reinforcement learning framework that enables a single policy to perform walking, running, and fall recovery on the Unitree G1 humanoid robot, validated on physical hardware without any explicit mode-switching command at deployment. The framework extends Adversarial Motion Priors (AMP) by replacing the conventional global reference distribution with a state-dependent gate that routes each training transition to one of two discriminators: a dedicated recovery discriminator and a velocity-conditioned locomotion discriminator that jointly covers walking and running. The gate is defined by a single fixed threshold on projected gravity: the recovery discriminator is activated when body tilt exceeds approximately $37^\circ$ from vertical ($|g_z+1|>0.6$); otherwise the locomotion discriminator is used, with the normalized commanded velocity serving as a condition that selects the appropriate reference trajectory between walk and run clips. Only three LAFAN1 reference clips are required to regularize the complete behavior set. At deployment, a single frozen ONNX policy executes at 50\,Hz with no runtime mode logic; hardware experiments demonstrate successful recovery from both prone and supine falls and smooth walk-to-run transitions under the same controller.
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