BLOG ILLUSTRATION / EXECUTION STORY

Prediction before a decision

A world model reads history, current state and a candidate executable action. The model predicts states or observations and optional reward/value. A decision step uses the predictions. Only deployment adds environment execution and new observations. The prediction trace stops at the decision.

Prediction before a decisionA world model reads history, current state and a candidate executable action. The model predicts states or observations and optional reward/value. A decision step uses the predictions. Only deployment adds environment execution and new observations. The prediction trace stops at the decision. Predictions for decisions Predictive context History + current state Candidate action Executable command World model Action-conditioned Predicted results State + optional reward Decision step Plan / evaluate / learn Execute + observe Deployment only
A world model receives context and a candidate robot action. Its predicted results enter a planner, evaluator, filter or policy learner. The executed action returns new observations. Author-created explanatory diagram. Playback time is illustrative and is not measured runtime. Open full-size SVG