World Models & Simulation
How machines form internal models of environments, not as still scenes, but as dynamic systems of objects, forces, constraints, paths, probabilities, and futures.
The world is not a still image, it is a field of possible events
Objects may fall, collide, roll, break, block, support, accelerate, decelerate, or disappear from view. Bodies may turn, reach, cross, stop, stumble, or change direction. A spatial system that understands only the present does not yet understand space.
We study how machines form internal models of environments, not merely as visual scenes, but as dynamic systems built from structure, constraint, motion, physics, memory, and the shape of the field.
Space contains futures
Internal models of a dynamic world
Objects & forces
We model environments as dynamic systems, objects acted on by forces, not pixels frozen in a frame. Anticipation begins with knowing what can move and what makes it move.
Constraints
What blocks, supports, and bounds, the rules a scene must obey before any future is possible.
Paths & probabilities
Where a body might move, where an object might land, which trajectories are likely and which may close.
Futures
The aim is anticipation: understanding consequence before consequence arrives. Simulation is also how a scarce sample becomes usable, a rare case can be modeled before it is encountered again, giving perception a way to meet conditions that are rare, dangerous, private, or expensive in reality.
Seeing only now
A system that understands only the present reports what is in view this instant, and nothing of what may happen next. It does not yet understand space.
Modeling what may happen next
Where might this body move? Where might this object land? What path may close, what force may accumulate, what consequence is approaching? Simulation is the study of anticipation.
A system that understands only the present has not yet understood space
Space contains futures. Objects may fall, collide, roll, break, block, support, accelerate, or disappear from view; bodies may turn, reach, cross, stop, stumble, or change direction. A model that reports only what is in frame this instant can describe a scene without anticipating any of it.
Simulation is how a machine forms an internal model of what may happen next, not by statistical guess alone but by structure: by constraint, by motion, by physics, by memory, by the shape of the field. It asks where a body might move, where an object might land, what path may close, what force may accumulate, what relation may become dangerous, what consequence is approaching.
It is also how a scarce sample becomes usable. A rare event, a dangerous condition, an expensive or private situation can be modeled before it is encountered again, giving perception a way to meet the world's edge cases before the world presents them. This is the same logic that makes synthetic data valuable: variation generated on purpose, ahead of the failure it prepares for.
The aim is anticipation, understanding consequence before consequence arrives, the difference between a system that records the world and one that can act safely within it.
A model that can imagine the next second of a scene is also a model that can be tested against it. Anticipation and verification are the same discipline seen from two sides, and both are what let a system be trusted to act in the world rather than only to describe it.