D-World: An Explicit World-Language-Action Model with Recursive Self-Improvement under Dual Control
Frontier large language models (LLMs) now reason about space well enough to program robots, yet the world models behind robot policies are implicit, held in network weights or predicted video, and cannot be inspected or corrected directly. We present D-World, an explicit world-language-action model built on the Sense, Plan and Act decomposition. A perception agent writes the scene as an executable digital twin using generated vision and geometry code. A planning agent searches for actions in simulation and, after each executed action chunk, revises the twin's object states and named physical parameters against what the robot measured. A parameter revision is accepted only if replaying the...
| Authors | Xuan Wen, Baichao Wang |
|---|---|
| Primary category | cs.RO |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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