Embodied Intelligence: The Key to Unblocking Generalized Artificial Intelligence

TL;DR

Embodied Intelligence (EI) is proposed as a foundational route to AGI by leveraging real-world body-environment interaction. The paper systematically analyzes EI through four modules—perception, decision-making, action, and feedback—and contrasts modular architectures with end-to-end systems, mapping each module to six AGI principles. It provides a taxonomy of architectural definitions, surveys recent progress (DL, LLMs, multimodal models), and highlights data, learning, and integration challenges. The findings argue that dynamic, environment-driven learning in EI is essential to bridge narrow AI and AGI and to enable robust, generalizable intelligence in real-world settings.

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.

Figures (7)

• Figure 1: The concept of embodied intelligence has gone through three important stages of development, culminating in today's definition; the concept embodies three fundamental principles for system-level architectural design. Although the concept was once marginalized, with breakthroughs in robotics, reinforcement learning, and multimodal learning, embodied intelligence has regained widespread attention in the context of the rapid development of artificial intelligence.
• Figure 2: An intelligent agent is composed of four major modules: the decision-making module, the perception module, the action module, and the feedback module2021Hegde2014Nogueira2020Xia2021Coumans2022Fernandez-Chaves2007Ganapathycoumans2021pybullet. The feedback module can conduct real-time monitoring and accumulate experience for the perception module, enabling the perception module to collect real-time environmental information, transmit the information to the decision-making module, and simultaneously adjust the operation of the decision-making module so that it can generate decisions for actions in the environment. The action module2018Shigemi receives instructions from the decision-making module and optimizations from the feedback module, and controls the physical carrier to complete various actions.
• Figure 3: The perception process of multimodal models typically involves five steps: The first step is data acquisition 2014Winkler2017Zou2018Guo, which aims to collect multidimensional perceptual data through various sensors; the second step is data preprocessing 2019Shorten2002Elaksher2016García, where the collected raw data undergoes cleaning, noise reduction, alignment, and other processes to ensure consistency across modalities; the third step is feature extraction and representation 2017Wu2021Lindemann2014Luo, where effective features are extracted from the raw data of each modality; the fourth step is data fusion, where data from different modalities are merged to enhance overall perception; and the fifth step is multimodal learning and inference, where the fused features are used for higher-level tasks.
• Figure 4: The decision-making module comprises four main functional components: environmental understanding and reasoning, task planning, decision generation, and learning and evolution. The environmental understanding and reasoning module 2004Michel2006Hohl extracts critical information from perceptual data to construct a comprehensive understanding of the current environment. The task planning module 2008Galindo2006Alami is responsible for decomposing tasks and formulating plans. The decision generation module 2019Paxton selects and executes specific actions based on the plans. These three components work closely together to ensure the efficiency and accuracy of the decision-making process. The learning and evolution framework 2021Gupta2004Floreano permeates the entire decision-making module, continuously optimizing decisions through methods such as reinforcement learning and meta-learning, leveraging environmental feedback to promote the adaptation and evolution of the intelligent agenthuang2021learning.
• Figure 5: The three main functions under the decision module are environment understanding and reasoning, task planning and decision generation. Through step-by-step continuous learning, the sensors complete the perception and comprehensive modeling of the environment, and transform high-level task objectives into executable subtasks, and formulate optimal execution paths, and finally generate and execute specific behaviors.