How to Build a Lightweight Vision-Language-Action-Inspired Embodied Agent with Latent World Modeling and Model Predictive Control

A tutorial shows how to train a compact vision-based embodied agent in a NumPy grid world using a latent world model and model predictive control.

The agent learns from RGB observations, predicts future latent states and frames, and selects actions in a closed loop.

This approach offers a lightweight way to combine perception, prediction, and planning from images for efficient embodied AI.