Zero-HLM: Generalizable Humanoid Loco-Manipulation without Real Robot Data

Learning generalizable humanoid loco-manipulation remains bottlenecked by costly real-world demonstrations. Simulation offers a scalable alternative, but extending it to humanoids requires solving two coupled challenges: constructing scene-scale environments that are both visually faithful and physics-ready, and generating successful demonstrations under unified whole-body control. We present Zero-HLM, a real-to-sim-to-real framework that transforms smartphone captures and simulated source demonstrations into scalable training data for humanoid loco-manipulation. We couple Gaussian visual reconstruction with agentic physical scene construction to build interaction-ready digital twins with aligned visual and physical geometry. Within the environment, our motion–environment co-adaptation strategy synthesizes unified whole-body references and uses rollout feedback to restore interaction geometry when tracking deviations disrupt execution. We successfully generate 4,800 physically validated demonstrations in simulation using Zero-HLM, averaging a 1.8× speedup over real-world data collection. Policies trained solely on these data transfer zero-shot to a Unitree G1 across three long-horizon tasks. With GR00T N1.7, simulation-only training achieves 66.7% mean full-task success with an absolute 20% gain over real-data training under matched configurations. Across five generalization settings, pooled real-world success increases from 23% to 69% without additional real demonstrations. To support systematic evaluation, we establish a benchmark with nine skill stages and shared protocols for comparing models and studying data scaling.