D-World: An Explicit World-Language-Action Model with Recursive Self-Improvement under Dual Control

What this work does

D-World represents a robot's environment as an executable digital twin that agents update from physical measurements. Proposed parameter changes are accepted only when replay does not worsen measured residuals, while action selection balances task progress against revealing model errors.