Humanoid robots can fall on slopes, gravel, and uneven ground in unstructured environments. We target integrated fall recovery and locomotion: rebuilding balance from a fallen state using proprioception alone and resuming velocity-commanded walking at the fall site. Prior methods often stop at quasi-static rise, neglect the post-fall ground-contact phase, or, when trained on mixed terrains without separating recovery and locomotion phases or per-surface constraints, collapse to a single compromise get-up across surfaces. We propose Phase--Terrain Decoupled Learning (PTDL), which decouples training supervision along phase and terrain axes while deploying one proprioceptive policy. On the phase axis, projected-gravity-gated dual motion-prior discriminators and a probe-to-walk transition link post-fall recovery to commanded walking. On the terrain axis, terrain-stratified recovery shaping assigns surface-specific training supervision on flat ground, gravel, and slopes; terrain labels are training-only and withheld from policy observations, enabling implicit post-fall strategy selection at deployment. We validate PTDL on a 29-DoF Unitree G1 across flat ground, gravel, and slopes up to 20 degrees in simulation and on hardware, achieving stable cross-terrain recovery, smooth recovery-to-locomotion transitions, and differentiated post-fall rise behaviors under one deployed policy.

翻译：摘要：人形机器人在非结构化环境中可能跌落在斜坡、碎石路面及不平整地面上。本文聚焦于集成式跌倒恢复与运动控制：通过纯本体感知恢复平衡状态，并在跌倒位置重新执行速度指令行走。现有方法通常止步于准静态起立，忽略跌倒后地面接触阶段，或在混合地形训练时未分离恢复与运动阶段及地表约束，导致跨地表起立行为趋同。我们提出相位-地形解耦学习（PTDL），该方法沿相位与地形轴解耦训练监督信号，同时部署单一本体感知策略。在相位轴上，采用投影重力门控双运动先验判别器与探测-行走过渡机制，连接跌倒后恢复与指令行走；在地形轴上，地形分层恢复塑形方法为平地、碎石路面及斜坡分配特定地表训练监督，地形标签仅用于训练阶段且不传入策略观测，实现部署时隐式后验策略选择。我们在29自由度宇树G1人形机器人上，通过仿真与硬件实验验证PTDL在平地、碎石路面及20度斜坡上的性能，实现稳定跨地形恢复、平滑恢复-运动过渡，并在一套部署策略下展现差异化的跌倒后起立行为。