Reliable fall recovery is critical for humanoids operating in cluttered environments. Unlike quadrupeds or wheeled robots, humanoids undergo high-energy impacts, complex whole-body contact, and large viewpoint changes during falls, making recovery essential. Prior work treats fall safety as separate problems (avoidance, impact mitigation, stand-up) or learns end-to-end policies without vision, typically on flat terrain. More fundamentally, it couples pose, dynamics, and terrain into a single data space, requiring exhaustive coverage and limiting generalization. We present a unified visually grounded fall-safety approach spanning all phases of recovery. It builds on two insights: (1) human fall-recovery poses are structured and transferable across terrains via alignment, and (2) effective recovery depends on interpreting the recovery goal in relation to the surrounding terrain. We train a privileged teacher using sparse human demonstrations and simulated terrains, and distill it into a deployable student that relies on egocentric depth and proprioception. The student predicts a goal-in-context latent representing the recovery target in relation to its surroundings, guiding coordinated whole-body reactions. Experiments in simulation and on a Unitree G1 demonstrate robust zero-shot fall safety across diverse terrains. Project page: https://vigor2026.github.io/.

翻译：暂无翻译