Physical AI and World Models: Concepts, Trends, and Open Source Ecosystem
Summary of the physical AI
Introduction Recent advances in AI are moving beyond perception-driven systems toward Physical AI , where models understand, predict, and interact with the physical world. This paradigm integrates perception, reasoning, modeling, and action into a unified framework. This document summarizes: itemize
Introduction
Recent advances in AI are moving beyond perception-driven systems toward Physical AI , where models understand, predict, and interact with the physical world. This paradigm integrates perception, reasoning, modeling, and action into a unified framework.
This document summarizes:
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Physical AI framework
World model concept
Relationship with embodied AI
Implementation strategies
Open-source ecosystem
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Physical AI Framework
Physical AI can be decomposed into four stages:
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Perception Reasoning World Model Interaction
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Perception
Extracts physical properties from sensory data:
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Object detection
3D understanding
Material and dynamics estimation
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Reasoning
Performs physical inference:
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Symbolic reasoning (equations, logic)
Intuitive physics (learned dynamics)
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World Model
An internal predictive model of the environment:
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s_ t+1 = f_ (s_t, a_t)
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Where:
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s_t : current state
a_t : action
f_ : learned dynamics model
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Interaction
Embodied execution:
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Robotics
Autonomous driving
Vision-Language-Action (VLA)
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World Models
Definition
A world model is:
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A learned function that predicts future states of the environment given current observations and actions.
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Two Types of World Models
Explicit (Physics Engine)
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External simulators (MuJoCo, Isaac Sim)
Accurate but limited and computationally expensive
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Implicit (Neural World Model)
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Learned dynamics (Dreamer, RSSM)
Flexible and generalizable
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Comparison
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tabular c|c|c
& Physics Engine & Neural World Model
Location & External & Internal
Accuracy & High & Approximate
Speed & Slow & Fast
Generalization & Limited & High
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Role of LLMs
LLMs are not physics simulators. Instead, they serve as high-level planners.
System Architecture
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Perception World Model LLM Planner Action
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Responsibilities
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World Model: Predict future states
LLM: Planning and reasoning
Controller: Execute actions
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Comparison with Other Paradigms
World Model vs Physical AI
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World Model: Prediction module
Physical AI: Full pipeline integration
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World Model Physical AI
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Embodied AI vs Physical AI
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Embodied AI: Focus on action and control
Physical AI: Focus on physical understanding
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Unified View
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Embodied AI Agent = World Model + Planner + Controller
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Open Source World Models
Latent World Models
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DreamerV3 ( https://github.com/danijar/dreamerv3 )
RSSM-based models
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Physics-based Systems
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MuJoCo
NVIDIA Isaac Sim
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Environment Platforms
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Habitat (Meta)
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Generative World Models
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Diffusion-based video models
Genie (interactive world models)
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Key Insights
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World models are internal simulators of reality
LLMs perform planning, not physics
Physical AI integrates perception, reasoning, modeling, and action
Future AI systems require causal and physics-grounded understanding
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Conclusion
The field is evolving from pattern recognition to physics-aware intelligence:
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Perception World Model Reasoning Action
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Physical AI represents the next step toward general intelligence grounded in the real world.