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About this paper
Yequan Wang, Aixin Sun
This paper reviews current Embodied AI capabilities and proposes a five-level taxonomy to map progress toward Embodied AGI. It outlines a conceptual framework, including an omnimodal model structure and integrated training paradigms, to guide the development of L3+ general-purpose embodied agents.
Key insights
- Embodied AI is presented as either an essential pathway to AGI or a core component of its definition, emphasizing physical grounding for general intelligence.
- Current state-of-the-art embodied AI systems primarily operate at L1-L2, demonstrating robustness in specific tasks but lacking broad inter-task generalization and human-like cognitive abilities.
- Achieving L3+ Embodied AGI requires addressing critical gaps in comprehensive joint-modal capabilities, humanoid cognition, real-time responsiveness, and restricted generalization.
Summary by alphaXiv.
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