WORLD MODELS · INTERACTIVE LANDSCAPE

World Model Landscape

Explore how world-model research connects representation, prediction, simulation, spatial intelligence, robotics, embodied AI, Physical AI, reinforcement learning and autonomous agents.

Observe→ Represent→ Predict→ Simulate→ Plan→ Act

Explore the landscape

—Visible categories
—Landscape layers
—Use cases
15Core areas

How the field fits together

RepresentationWorld models begin by constructing useful internal states from observations, sensors, language, video or other modalities.
DynamicsThey learn how states evolve through time and how actions can change the environment.
PredictionFuture states, observations, trajectories, rewards or outcomes can be estimated before they occur.
SimulationPossible futures can be generated or imagined without executing every action in the real world.
PlanningAgents can compare possible trajectories and select actions that better align with a goal.
ActionRobots, software agents and autonomous systems close the loop by acting and observing the result.

What are World Models?

A world model is an AI model that learns an internal representation of an environment and aspects of how that environment changes. The important idea is not only understanding the present, but also modeling possible futures and the consequences of actions.
State+ Action→ World Model→ Possible Future State