PyTorch World
A shared world for world-model research.
Tools for intelligence that understands the world.
Better tools for the next generation of world models.
PyTorch WorldA shared world for world-model research.
Where we see world models making a difference.
Helping robots understand their surroundings, anticipate change, and plan actions in the physical world.
Exploring agent behaviour in simulated scenarios to evaluate decisions before they reach the real world.
Modelling connected systems to explore scenarios, understand dependencies, and inform decisions.
World models learn how an environment changes. By predicting in a learned representation, they can imagine outcomes and help plan the next action.
Our first toolkit brings together joint-embedding architectures, action-conditioned models, and the environments to explore them.
Explore our researchFollow the work. Bring your ideas.
Find us on GitHub