World-Action ModelsExpert
Direct Experience World-Model Optimization: Learning the World Beyond Action Imitation
This arXiv preprint introduces DEWO, a post-deployment learning method for world-action models that learns from observed visual futures around interaction turning points, not just action imitation. Across five simulated dexterous tasks, DEWO improves average success for multiple world-action model variants, and on two real dexterous-hand platforms it lifts success on initially viable spatial cells from 51.0% to 71.7% over two deployment rounds. The work matters because it shows continued predictive learning from deployment experience can make dexterous policies more reliable.