# world model Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/world-model
> Markdown URL: https://aitinkerers.org/technologies/world-model.md
> Technology record last updated: 2026-04-20T14:13:33Z
> Generated: 2026-09-21T11:43:45Z

An AI architecture that learns a compressed spatial and temporal representation of an environment to simulate future outcomes.

World models enable agents to internalize environment dynamics by training a Variational Autoencoder (VAE) to compress visual inputs and a Recurrent Neural Network (RNN) to predict future states. Ha and Schmidhuber demonstrated this in 2018 using the Car Racing and VizDoom benchmarks, where agents trained entirely within their own mental simulations successfully transferred skills to the real environment. By decoupling perception from reasoning, these systems reduce sample complexity and allow for hallucinated training scenarios (DreamerV3), making them foundational for autonomous robotics and complex decision-making tasks.

- Official technology site: https://worldmodels.github.io
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Using world models for grounding long horizon robotics tasks](https://singapore.aitinkerers.org/talks/rsvp_jLKAZdOnVHg)

In frontier research, VLMs and world models are being used for planning and subgoal image generation for grounding long-horizon robotics tasks. In the open-source community, we are building on top of open-source models to reproduce these results and deploy them in industry to improve performance in embodied AI use cases.

- Event context: AI Tinkerers Singapore: The Agentic Future & Dev/Eng Workflows — 2026-04-21 — Singapore
- Public talk page: https://singapore.aitinkerers.org/talks/rsvp_jLKAZdOnVHg

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