Embodied Intelligence: The Key to Unblocking Generalized Artificial Intelligence

• URL: http://arxiv.org/abs/2505.06897v1
• Date: Sun, 11 May 2025 08:29:20 GMT
• Title: Embodied Intelligence: The Key to Unblocking Generalized Artificial Intelligence
• Authors: Jinhao Jiang, Changlin Chen, Shile Feng, Wanru Geng, Zesheng Zhou, Ni Wang, Shuai Li, Feng-Qi Cui, Erbao Dong,
• Abstract summary: Embodied Artificial Intelligence (EAI) involves intelligent systems with physical presence and real-time interaction with the environment.<n>EAI's integration of dynamic learning and real-world interaction is essential for bridging the gap between narrow AI and AGI.
• Score: 12.875350514584877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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