$Ψ_0$: An Open Foundation Model Towards Universal Humanoid Loco-Manipulation

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• $Ψ_0$ decouples the learning process to maximize the utility of heterogeneous data sources for humanoid loco-manipulation.method 70%
• A staged training paradigm is proposed, involving autoregressive pre-training of a VLM backbone on human videos followed by flow-based action expert post-training on humanoid robot data.method 70%
• Pre-training on high-quality egocentric human manipulation data followed by post-training on domain-specific real-world humanoid trajectories yields superior performance compared to scaling with noisy Internet clips or heterogeneous cross-embodiment robot datasets.method 70%
• $Ψ_0$ achieves the best performance using approximately 800 hours of human video data and 30 hours of real-world robot data.result 70%
• $Ψ_0$ outperforms baselines pre-trained on more than 10 times as much data by over 40% in overall success rate across multiple tasks.result 70%
• The research will open-source the entire ecosystem, including a data processing and training pipeline, a humanoid foundation model, and a real-time action inference engine.technical 70%
• Potential limitations include the cost and complexity of deploying advanced humanoid systems at scale and the need for specific tuning for different task domains.limitation 70%

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