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

Xuan Wen; Baichao Wang

cs.ROOct 12, 2026

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

Authors: Xuan Wen, Baichao Wang

Organizations: Geek+

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.

Figures & tables

Figure 1: D-World , an explicit world-language-action model. Implicit models (left) keep the world in network weights or predicted video. D-World (right) writes it as an executable twin. Sense constructs the twin, Plan proposes trajectories and twin revisions, and a gate checks each action chunk before execution. Measurements of the real robot drive state correction and replay-checked parameter revision (RSI), and the dashed path requests reconstruction. In a bread-grasp episode, the twin shows both grippers open, ACTION shows the robot holding a lifted bread slice, and the measurement pair compares this post-lift observation with the reconstructed twin. In the language channel, Plan asks and Sense answers.

Figure 2: Replay-checked revision after a failed lift. In a failed bread grasp, the real bread remains in its rack while the twin lifts it with the gripper. Reducing the modeled thickness reproduces the slip during replay and reduces both the depth and the closure residual. Friction, to which this chunk is insensitive, is left unchanged. The revision passes the non-regression check and is accepted, and the next chunk attempts another grasp without reconstruction. Here $j$ is the latest executed round and $t$ the revision round. An earlier thickness proposal had increased the residual on the preceding chunk and was rejected.

Panel of Figure 3 — Figure 3: Towel handling. (a) The top row shows camera images and the initial twin rendered from the same cameras, and the bottom row selected interaction and reconstruction stages, numbered by chunk. Dashed outlines mark the grasp mismatch between the twin and the robot, after which reconstruction changes the towel representation to match its observed configuration. (b) Where the time goes in the same episode. Sense is blue, lighter while it answers a question, Plan is orange, and the robot’s chunks are green with the numbers of (a). The bottom lanes magnify the three chunks between the two reconstructions. Model inference fills almost the entire hour, whereas the robot moves for seconds per chunk.

Case	Revision	Residual	Before	After
Slot support	support inside the slot raised by 2 cm	bread depth	22.4 mm	1.5 mm
Towel geometry	table aligned with the measured plane and towel thickness scaled by 1.45	towel depth	60.1 mm	4.3 mm
Bread grasp	bread thickness reduced from 20 to 12 mm	gripper closure	0.076	0.035
		bread depth	201 mm	11 mm

Table 1: Replay residuals before and after parameter revision. Depth entries are absolute signed-median residuals, maximized over the available cameras. Closure is dimensionless on a scale from 0 (open) to 1 (closed).

Figure 4: From episodes to high-fidelity worlds. (a) Each episode records observations, twins, controls and revisions. (b) Versioned libraries collect agent-written skills, such as perception and motion primitives, and scene bodies from completed episodes, whose parameters RSI has validated against the robot’s measurements. (c) Offline, recorded chunks are replayed jointly to refit parameters, and the refitted twin is rendered again with the same geometry, here in MuJoCo and in Blender. Skills and assets can be reused in later episodes, and refitted twins can serve post-training.

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

Symbol	Meaning	Value
	max. end-effector step per action	5 cm, 0.35 rad
	end-effector deviation for monitor alert	3 cm
	gripper closure deviation for monitor alert	0.5
	end-effector deviation for monitor stop	10 cm
$\varepsilon^{\mathrm{d}}$	depth tolerance at distance $z$	$\max(0.02z,\kappa z^{2})$
$\kappa$	depth noise coefficient, per observation	0.014 to 0.10 m -1

Appendix

Table 2: Operational values and episode budgets. The rollout budget is fixed within an episode, and the depth coefficient is estimated per observation.

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