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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2505.06897 |
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| _version_ | 1866915282838093824 |
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| author | Jiang, Jinhao Chen, Changlin Feng, Shile Geng, Wanru Zhou, Zesheng Wang, Ni Li, Shuai Cui, Feng-Qi Dong, Erbao |
| author_facet | Jiang, Jinhao Chen, Changlin Feng, Shile Geng, Wanru Zhou, Zesheng Wang, Ni Li, Shuai Cui, Feng-Qi Dong, Erbao |
| contents | The ultimate goal of artificial intelligence (AI) is to achieve Artificial General Intelligence (AGI). Embodied Artificial Intelligence (EAI), which involves intelligent systems with physical presence and real-time interaction with the environment, has emerged as a key research direction in pursuit of AGI. While advancements in deep learning, reinforcement learning, large-scale language models, and multimodal technologies have significantly contributed to the progress of EAI, most existing reviews focus on specific technologies or applications. A systematic overview, particularly one that explores the direct connection between EAI and AGI, remains scarce. This paper examines EAI as a foundational approach to AGI, systematically analyzing its four core modules: perception, intelligent decision-making, action, and feedback. We provide a detailed discussion of how each module contributes to the six core principles of AGI. Additionally, we discuss future trends, challenges, and research directions in EAI, emphasizing its potential as a cornerstone for AGI development. Our findings suggest that EAI's integration of dynamic learning and real-world interaction is essential for bridging the gap between narrow AI and AGI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06897 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Embodied Intelligence: The Key to Unblocking Generalized Artificial Intelligence Jiang, Jinhao Chen, Changlin Feng, Shile Geng, Wanru Zhou, Zesheng Wang, Ni Li, Shuai Cui, Feng-Qi Dong, Erbao Artificial Intelligence The ultimate goal of artificial intelligence (AI) is to achieve Artificial General Intelligence (AGI). Embodied Artificial Intelligence (EAI), which involves intelligent systems with physical presence and real-time interaction with the environment, has emerged as a key research direction in pursuit of AGI. While advancements in deep learning, reinforcement learning, large-scale language models, and multimodal technologies have significantly contributed to the progress of EAI, most existing reviews focus on specific technologies or applications. A systematic overview, particularly one that explores the direct connection between EAI and AGI, remains scarce. This paper examines EAI as a foundational approach to AGI, systematically analyzing its four core modules: perception, intelligent decision-making, action, and feedback. We provide a detailed discussion of how each module contributes to the six core principles of AGI. Additionally, we discuss future trends, challenges, and research directions in EAI, emphasizing its potential as a cornerstone for AGI development. Our findings suggest that EAI's integration of dynamic learning and real-world interaction is essential for bridging the gap between narrow AI and AGI. |
| title | Embodied Intelligence: The Key to Unblocking Generalized Artificial Intelligence |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2505.06897 |