# Embodied Intelligence: The Key to Unblocking Generalized Artificial Intelligence

arXiv: https://arxiv.org/abs/2505.06897 · HTML: https://mlsanity.com/paper/2505.06897/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 139 references that are in the map.
- First posted on arXiv 11 May 2025.

## Builds on (most cited first, 30 of 30)

- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Large Scale Incremental Learning](https://mlsanity.com/paper/1905.13260/) (2019)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [A Theoretical Analysis of Deep Q-Learning](https://mlsanity.com/paper/1901.00137/) (2019)
- [OpenAGI: When LLM Meets Domain Experts](https://mlsanity.com/paper/2304.04370/) (2023)
- [Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI](https://mlsanity.com/paper/2407.06886/) (2024)
- [Embodied Intelligence via Learning and Evolution](https://mlsanity.com/paper/2102.02202/) (2021)
- [Deep Hierarchical Semantic Segmentation](https://mlsanity.com/paper/2203.14335/) (2022)
- [Multi-modal Sensor Fusion for Auto Driving Perception: A Survey](https://mlsanity.com/paper/2202.02703/) (2022)
- [DenseFusion-1M: Merging Vision Experts for Comprehensive Multimodal Perception](https://mlsanity.com/paper/2407.08303/) (2024)
- [SkillDiffuser: Interpretable Hierarchical Planning via Skill Abstractions in Diffusion-Based Task Execution](https://mlsanity.com/paper/2312.11598/) (2023)
- [MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perception](https://mlsanity.com/paper/2312.07472/) (2023)
- [Visual Semantic Role Labeling for Video Understanding](https://mlsanity.com/paper/2104.00990/) (2021)
- [The Unreasonable Effectiveness of Deep Learning in Artificial Intelligence](https://mlsanity.com/paper/2002.04806/) (2020)
- [Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusion](https://mlsanity.com/paper/2006.08159/) (2020)
- [Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image](https://mlsanity.com/paper/2006.05398/) (2020)
- [Multi-modal Sensor Fusion-Based Deep Neural Network for End-to-end Autonomous Driving with Scene Understanding](https://mlsanity.com/paper/2005.09202/) (2020)
- [Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving](https://mlsanity.com/paper/1903.10654/) (2019)
- [From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence](https://mlsanity.com/paper/2110.15245/) (2021)
- [Hybrid Inverse Reinforcement Learning](https://mlsanity.com/paper/2402.08848/) (2024)
- [Prompt, Plan, Perform: LLM-based Humanoid Control via Quantized Imitation Learning](https://mlsanity.com/paper/2309.11359/) (2023)
- [Recent Advances and Trends in Multimodal Deep Learning: A Review](https://mlsanity.com/paper/2105.11087/) (2021)
- [Learning a Decision Module by Imitating Driver's Control Behaviors](https://mlsanity.com/paper/1912.00191/) (2019)
- [MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning](https://mlsanity.com/paper/2403.09859/) (2024)
- [Visual Robot Task Planning](https://mlsanity.com/paper/1804.00062/) (2018)
- [ACCNet: Actor-Coordinator-Critic Net for "Learning-to-Communicate" with Deep Multi-agent Reinforcement Learning](https://mlsanity.com/paper/1706.03235/) (2017)
- [Enhancing Social Decision-Making of Autonomous Vehicles: A Mixed-Strategy Game Approach With Interaction Orientation Identification](https://mlsanity.com/paper/2312.11843/) (2023)
- [A Study of Forward-Forward Algorithm for Self-Supervised Learning](https://mlsanity.com/paper/2309.11955/) (2023)
- [Artificial Human Intelligence: The role of Humans in the Development of Next Generation AI](https://mlsanity.com/paper/2409.16001/) (2024)

## Built on by (most cited first, 1 of 1)

- [STORM: Search-Guided Generative World Models for Robotic Manipulation](https://mlsanity.com/paper/2512.18477/) (2025)

## Abstract (arXiv)

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.