外观
论文arXiv 2610.1711416 天前
D-World：双重控制下具有递归自我改进的显式世界-语言-动作模型
原文链接：D-World: An Explicit World-Language-Action Model with Recursive Self-Improvement under Dual Control（arXiv 2610.17114 · 发布日期：2026-10-12）
D-World: An Explicit World-Language-Action Model with Recursive Self-Improvement under Dual Control
为解决机器人世界模型难以直接检查纠正，D-World用可执行数字孪生显式建模，并在双臂机器人任务上验证。
核心要点
- 现有机器人策略的世界模型多为隐式，难以被直接检查或纠正。
- D-World基于Sense、Plan、Act，将场景写成可执行数字孪生。
- 规划代理在仿真中搜索动作，执行后按测量修订状态与物理参数。
- 双臂机器人毛巾和烤面包机任务显示，局部修订可让执行继续。
验证范围与局限：验证范围为双臂机器人上的毛巾和烤面包机任务。
英文摘要 (Abstract)
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 latest chunk does not increase any measured residual beyond its tolerance, and every chunk is checked in the updated twin before it runs. Structural mismatches return to perception for reconstruction. We call this measurement-grounded cycle of correction and revision recursive self-improvement (RSI), in which the LLM stays frozen while the world it acts in improves. We model the choice of actions that both advance the task and expose model error as a dual control problem and show that the cost of incorporating evidence into the twin sets the threshold at which probing pays, so cheap local revision counteracts the turn-off of cautious controllers. Each episode runs a real-to-sim-to-real loop, and its recorded twins can be refitted and re-rendered offline into higher-fidelity environments for post-training. Because perception and planning communicate only through the twin and language, either can be replaced by a learned model, and since inference dominates the loop, the loop speeds up with faster models. On a dual-arm robot, towel and toaster tasks show local revisions, such as one reducing the bread's rendered-depth residual from 22.4 to 1.5 mm, that let execution continue without reconstruction.
作者：Xuan Wen, Baichao Wang
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