Falling is an inherent risk for humanoid robots operating in unstructured environments. Existing reinforcement learning methods that leverage expert motion priors are predominantly trained on flat-ground fall-recovery tasks and typically rely on hard switching between separate recovery and locomotion controllers. As a result, such policies struggle to achieve smooth and robust recovery behaviors when deployed on complex terrains such as slopes and gravel. This paper presents \textbf{CG-MuTra}, a unified continuously-gated multi-scale discriminator framework for multi-terrain adaptive fall recovery. CG-MuTra introduces a proprioceptively-derived continuous gate $α= f(z_{\mathrm{root}}, s)$ that softly blends three discriminators operating at different temporal horizons: frame-level stability ($Φ_{\mathrm{frame}}$, $H=1$), temporal smoothness ($Φ_{\mathrm{seq}}$, $H=5$), and gait periodicity ($Φ_{\mathrm{gait}}$, $H=10$). This design enables seamless recovery-to-locomotion transitions without explicit mode switching. Furthermore, we propose a Terrain-Pose Risk Coupling Sampler (TPRCS) that explicitly couples dangerous edge initial poses with terrain dynamics during training, forming a closed-loop synergy with the terrain-privileged shaping term $Ξ_κ$. We validate CG-MuTra on a Unitree G1 humanoid across grass, slopes ($10^\circ$--$15^\circ$), and gravel in both simulation and hardware. Experimental results demonstrate that CG-MuTra achieves smooth, highly robust fall recovery and locomotion transitions across multiple terrains while maintaining a single deployable policy.

翻译：暂无翻译