A leading scientist in embodied AI, Luo Jianlan, argues that the field should not simply replicate the scaling laws of large language models. Instead, he emphasizes the critical need for a closed-loop system where real-world deployment continuously feeds data back into training. Luo highlights that current "pre-training" of embodied models is often more akin to mid-training or fine-tuning due to a scarcity of high-quality, diverse real-world interaction data. He proposes three key technical pillars: SOP for scalable online post-training infrastructure, LWD for in-deployment learning and continuous evolution, and the τ0-WM world model for predictive physics-based action selection. AI
IMPACT Embodied AI development requires a shift from LLM-like scaling to real-world deployment feedback loops for true progress.
RANK_REASON The article discusses novel research and technical proposals for embodied AI, including new concepts like SOP, LWD, and a world model (τ0-WM), presented by a leading scientist in the field. [lever_c_demoted from research: ic=1 ai=1.0]
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