Embodied Intelligence: The Key to Unblocking Generalized Artificial Intelligence

TL;DR

This paper explores embodied artificial intelligence (EAI) as a foundational approach to achieving artificial general intelligence (AGI), emphasizing the importance of real-world interaction and dynamic learning in bridging the gap from narrow AI.

Contribution

It systematically analyzes EAI's core modules and their connection to AGI principles, providing a comprehensive overview and future research directions.

Findings

EAI's integration of perception, decision-making, action, and feedback is crucial for AGI.

Real-time interaction and dynamic learning are key to bridging narrow AI and AGI.

EAI offers a promising pathway towards achieving AGI.

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.