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Zero-HLM: A humanoid loco-manipulation framework without real robot data

Zero-HLM proposes a real-to-sim-to-real framework that turns smartphone captures and simulated source demonstrations into scalable training data for humanoid loco-manipulation. The study generated 4,800 physically validated demonstrations and achieved zero-shot transfer on three Unitree G1 tasks; GR00T N1.7 trained in simulation reached a mean full-task success rate of 66.7%, versus 46.7% with real-data training.

Part of the event: Zero-HLM: Humanoid Robots Trained on Simulated Data Outperform Real-Robot Data

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