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Embodied Intelligence
Overview
Embodied Intelligence Overview
Embodied Intelligence Roadmap
Key Conferences & Journals
Milestones in Embodied AI
Theoretical Research
Robotics Fundamentals
Robot Learning
Models & Algorithms
Software Platforms
Hardware
Robot Forms
Real-World Deployment
Industry Ecosystem
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Table of Contents
Introduction
What is Embodied Intelligence
Definition
The Essence of the Body
The Embodied Nature of Intelligence
Historical Development of Embodied Intelligence
Philosophical and Cognitive Science Foundations (1940s-1990s)
Behavior-Based Robotics (1986-2000s)
The Deep Learning Turning Point (2013-2020)
The Foundation Model Era (2022-Present)
Core Pillars of Embodied Intelligence
1. Perception
2. World Model
3. Planning and Decision Making
4. Action and Control
5. Learning
6. Memory
Technical Paradigms of Embodied Intelligence
Paradigm 1: From Modular to End-to-End
Paradigm 2: Foundation Model-Driven Embodied Intelligence
Paradigm 3: Sim-to-Real (Sim2Real)
Paradigm 4: Data Scaling
Classification of Embodied Agents
Physical Robots (Robotic Agents)
Virtual Embodied Agents (VEA)
Boundary Discussion on Wearable Agents
Core Challenges of Embodied Intelligence
1. Data Bottleneck
2. Insufficient Generalization
3. Long-Horizon Task Planning
4. Safety and Robustness
5. Hardware Limitations
6. Lack of Evaluation Standards
7. Real-Time Requirements
8. From Lab to Real World
Relationship with Related Disciplines
Relationship with AI Agents
Relationship with Traditional Robotics
Relationship with Cognitive Science
Relationship with Computer Vision
Relationship with Autonomous Driving
Site Navigation Guide
References
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