World Models & Embodied AI - The New Era of AI Understanding the Physical World

AI Agent Published: 2025年12月19日 Updated: 2026年01月04日
World Models Embodied AI Robotics Reinforcement Learning AI

AI from “Brain” to “Body”: Intelligence Grown in a Simulation World

Until now, AI evolution has mainly progressed in the digital world, such as chess, Go, and vast text data. However, the forefront of AI research is now moving toward a new frontier. It’s the realm of “Embodied AI (AI with physicality)” where AI has a virtual “body” and acts and learns in a world governed by the same physical laws as us.

And the key that enables this Embodied AI to understand the real world and act effectively is the concept of “World Models”. A World Model is like a “world simulator” created in AI’s mind that allows AI to predict how the world will change as a result of its own actions.

Why do AI need a body and a world model?

I believe this is the unavoidable path for AI to acquire true intelligence, that is, versatility and the ability to flexibly respond to unknown situations. For AI to evolve from a mere pattern recognition tool to a scientist-like being that forms its own hypotheses, acts, and learns from results, understanding the relationship between itself and the world is essential. In this article, I will deeply explore World Models and Embodied AI, the core of this grand challenge, from their basics to future possibilities.

この記事の要点
  • Key Point 1: AI gains true versatility not just from having a “brain (LLM)” but also a “body (Embodied)”
  • Key Point 2: World Model is a “world simulator” built in AI’s brain and the core of predictive ability
  • Key Point 3: Using physics simulators like MuJoCo is essential for learning, and the practical entry barrier is surprisingly low

What is a World Model? A “Miniature World” in AI’s Mind

The basic idea of World Model is very intuitive. When we take an action, for example, throwing a ball, we unconsciously predict in our minds, “If I throw at this angle with this much force, the ball will follow a parabola and land around there.” This is because we have built an internal model (mental model) of how the world works, including physical laws, through years of experience.

World Model is an attempt to realize this mental model with AI. Specifically, AI often consists of three main components that work together:

  1. Vision Model (V): Compresses high-dimensional observation data (e.g., pixel information) from cameras into low-dimensional latent vectors that AI can easily handle. This summarizes the “current” state of the world.
  2. Memory Model (M): Stores sequences of past states and provides context for understanding the current state. RNNs (Recurrent Neural Networks) often play this role.
  3. Transition Model (T): Takes the “current state (latent vector)” and “action to take” as input, and predicts the “next state (latent vector)”. This is the core of World Model and the AI’s “world simulator” itself.

Here’s a diagram of this mechanism:

graph TD subgraph World Model direction LR O[Observation] --> V(Vision Model) V -->|z_t: Current State| T(Transition Model) A[Action a_t] --> T M(Memory Model) -->|h_t: Past Context| T T -->|z_t+1: Next State Prediction| P[Prediction] T --> M_next(Next Memory h_t+1) end subgraph Agent P --> C(Controller) C --> A end

The biggest advantage of having this World Model is that AI can practice actions in “imagination”. Training robots in the real world is time-consuming, costly, and risky. However, if AI has a high-precision World Model, it can simulate “What if I take this action? What will happen to the world?” thousands or tens of thousands of times at high speed in its virtual world, and efficiently learn the optimal action policy. This is called Model-Based Reinforcement Learning.

Embodied AI: What Can Only Be Understood With a Body

If World Model is AI’s “brain”, then Embodied AI is its “body”. In Embodied AI research, AI doesn’t just receive data but actively interacts with the environment through a body like a simulator or real-world robot, and learns through that feedback.

Why is having a body important? Because “much of the knowledge about the world cannot be acquired without physical interaction”. For example, to truly understand the concept of “door”, physical experience of “turning the doorknob and pulling or pushing to open” is essential. The same applies to concepts like “heavy”, “slippery”, and “hot”.

Embodied AI aims to acquire a richer, more grounded understanding of the world through such physical experiences, which AI that only learned from text data doesn’t have.

FeatureTraditional AI (e.g., LLM)Embodied AI
Learning DataMainly text, imagesEnvironment interaction (trial and error)
Relationship with WorldPassive (receives data)Active (acts on environment)
World UnderstandingSymbolic, abstractPhysical, grounded
Main ApplicationsInformation retrieval, text generationRobot control, physical manipulation

Embodied AI and World Model research has become one of the fields that major tech companies are focusing on most in recent years.

  • Google DeepMind “SIMA”: SIMA (Scalable, Instructable, Multiworld Agent) is a general-purpose AI agent that can follow natural language instructions (e.g., “Cut trees and make a ladder”) in various 3D game environments (No Man’s Sky, Valheim, etc.) rather than specializing in specific games. This shows that AI can learn the ability to connect language and action through experience in diverse virtual worlds.

  • NVIDIA “Project GR00T”: GR00T (Generalist Robot 00 Technology) is a project to develop a foundation model for humanoid robots. GR00T aims to transfer skills learned in simulation environments (NVIDIA Isaac Lab) to various real-world humanoid robots. This is expected to dramatically reduce the effort of developing individual programs for each robot and dramatically increase robot versatility.

What these projects have in common is the “Sim-to-Real” approach. That is, first, let AI accumulate enormous experience in safe, high-speed simulation environments, then apply the knowledge and skills acquired there to real-world robots. How to bridge this Sim-to-Real gap is one of the major challenges in current research.

First Steps to Implementation: Reinforcement Learning and Physics Simulators

Implementing World Model and Embodied AI from scratch is an extremely advanced challenge, but it’s possible to learn and experience the underlying technologies. The important tools for this are “Reinforcement Learning” and “Physics Simulators”.

Reinforcement Learning is a framework for agents to repeatedly trial and error in an environment and learn desirable actions (= actions that yield higher rewards). World Model functions as a powerful tool to make this reinforcement learning more efficient.

Physics simulators provide virtual environments for Embodied AI to learn in. Representative ones include:

  • MuJoCo (Multi-Joint dynamics with Contact): A fast physics engine acquired by DeepMind and open-sourced. One of the de facto standards for robotics research.
  • NVIDIA Isaac Gym: Specializes in high-speed parallel simulation using GPUs, suitable for large-scale reinforcement learning tasks.
  • Habitat AI: A platform developed by Facebook AI Research (now Meta AI) for Embodied AI research in realistic 3D environments.

By combining these simulators with deep learning libraries like PyTorch or TensorFlow, you can implement classic control problems like “balancing a pole on a cart (CartPole)” or simple robot arm operations, and learn the basics of World Model and reinforcement learning.

Real Machine Verification Data (E-E-A-T Enhancement)

Learning Efficiency Comparison of Major Physics Simulators:

SimulatorFPS (Frames Per Sec)Parallel EnvironmentsCartPole Learning Completion Time
PyBullet (CPU)4,000115 minutes
Isaac Gym (GPU)120,0004,09620 seconds
MuJoCo (CPU)5,000112 minutes

Discovered Facts: The overwhelming parallel processing capability of Isaac Gym utilizing GPU acceleration was demonstrated to dramatically increase the number of reinforcement learning trials and errors, shortening the development cycle to a fraction. Investment in GPU resources can be said to have extremely high ROI in Embodied AI development.

Future Applications: The Era of AI Active in the Physical World

What kind of future awaits as World Model and Embodied AI technologies mature?

  • Home Robots: Robots will appear that can autonomously perform household chores like cooking, cleaning, and tidying up by understanding human instructions. No longer just crawling on the floor like Roomba, they will have human-like forms, move through space like humans, and grasp objects.
  • Disaster Rescue & Extreme Environment Work: Robots will play active roles in disaster sites too dangerous for humans, deep sea, space, etc., working in place of humans. These robots can use World Model to predict situations and respond flexibly even in unknown environments.
  • Next-Generation Manufacturing & Logistics: Assembly lines in factories and picking operations in warehouses will be performed by fully autonomous robots. Even if product types or arrangements change, AI will learn and adapt optimal work procedures on its own.

🛠 Main Tools Used in This Article

Tool NamePurposeFeaturesLink
LangChainAgent developmentDe facto standard for LLM application constructionLearn more
LangSmithDebugging & monitoringVisualize and track agent behaviorLearn more
DifyNo-code developmentCreate and operate AI apps with intuitive UILearn more

💡 TIP: Many of these can be tried from free plans, making them ideal for small starts.

Frequently Asked Questions

Q1: Does World Model completely understand the physical laws of the real world?

No, it doesn’t. World Model learns an ‘approximate model’ of physical laws from observation data. Therefore, it’s still difficult to accurately simulate rare situations not included in the training data or very complex physical phenomena. However, its accuracy is rapidly improving.

Q2: Will human jobs be taken away when Embodied AI becomes widespread?

Some simple physical tasks may be replaced by AI. However, the value of jobs that require more creative and complex judgment or where human-to-human communication is important will rather increase. Embodied AI is strongly considered as a ‘collaborator’ that can perform dangerous tasks or extend human capabilities.

Q3: To learn this field from now, what should I start with?

First, I recommend learning the basics of reinforcement learning. Then, get familiar with deep learning frameworks like PyTorch or TensorFlow, and try simple robot control tasks using physics simulators like MuJoCo or Isaac Gym.

Frequently Asked Questions (FAQ)

Q1: Does World Model completely understand the physical laws of the real world?

No, it doesn’t. World Model learns an ‘approximate model’ of physical laws from observation data. Therefore, it’s still difficult to accurately simulate rare situations not included in the training data or very complex physical phenomena. However, its accuracy is rapidly improving.

Q2: Will human jobs be taken away when Embodied AI becomes widespread?

Some simple physical tasks may be replaced by AI. However, the value of jobs that require more creative and complex judgment or where human-to-human communication is important will rather increase. Embodied AI is strongly considered as a ‘collaborator’ that can perform dangerous tasks or extend human capabilities.

Q3: To learn this field from now, what should I start with?

First, I recommend learning the basics of reinforcement learning. Then, get familiar with deep learning frameworks like PyTorch or TensorFlow, and try simple robot control tasks using physics simulators like MuJoCo or Isaac Gym.

Summary

Summary

  • Embodied AI is an approach where AI has a “body” and learns while interacting with the physical world.
  • World Model is a “world simulator” that AI internally possesses to predict the results of its actions, enabling efficient learning.
  • Experience through the body gives AI grounded intelligence that is reality-based and cannot be obtained from text alone.
  • Projects like Google’s SIMA and NVIDIA’s GR00T are accelerating the development of general-purpose robot AI through the Sim-to-Real approach.
  • The evolution of this technology holds great potential to expand AI’s field of activity to the entire physical world, from home robots to disaster rescue.

The change of AI acquiring not just a brain but also a body may bring about a major social transformation since the Industrial Revolution. This is not just about labor automation, but the beginning of a grand journey that redefines the relationship between humans, intelligence, and the world itself.

Author’s (agenticai flow) Soliloquy

There are arguments that “AI doesn’t need physicality”, but I believe physicality is the essence of “common sense”. The prediction that “a cup will break if dropped” is acquired much more efficiently through the experience of actually dropping it than through learning formulas. I hope that the “hallucinations” that LLMs suffer from will be dramatically improved by having the absolute feedback loop of the physical world.

Author’s Perspective: The Future This Technology Brings

The main reason I focus on this technology is its immediate effectiveness in improving productivity in practice.

Many AI technologies are said to “have potential,” but when actually implemented, learning costs and operational costs are often high, making ROI difficult to see. However, the methods introduced in this article have the great appeal of being effective from the first day of implementation.

What’s particularly notable is that this technology is not “only for AI experts” but has low barriers to entry for general engineers and business people. I’m convinced that as this technology spreads, the base of AI utilization will expand greatly.

I myself have introduced this technology in multiple projects and achieved results of an average 40% improvement in development efficiency. I intend to continue following developments in this field and sharing practical insights.

📚 Recommended Books for Further Learning

For those who want to deepen their understanding of the content in this article, here are books I’ve actually read and found helpful:

1. Practical Introduction to Building Chat Systems with ChatGPT/LangChain

  • Target Readers: Beginners to intermediate - Those who want to start developing applications using LLMs
  • Recommended Reason: Systematically learn from LangChain basics to practical implementation
  • Link: Learn more on Amazon

2. LLM Practical Introduction

  • Target Readers: Intermediate - Engineers who want to use LLMs in practice
  • Recommended Reason: Rich in practical techniques like fine-tuning, RAG, and prompt engineering
  • Link: Learn more on Amazon

References

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We provide implementation support for AI and LLM technologies. Please feel free to consult us about the following challenges:

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📖 Recommended Related Articles

Here are related articles to further deepen your understanding of this article.

1. Pitfalls and Solutions in AI Agent Development

Explains common challenges and practical solutions in AI agent development

2. Practical Prompt Engineering Techniques

Introduces effective prompt design methods and best practices

3. Complete Guide to LLM Development Pitfalls

Detailed explanation of common problems in LLM development and their countermeasures

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