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DreamTrue: Action-Faithful Robot World Model with Counterfactual Post-Training

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Briefings

DreamTrue is a multi-view robot world model that achieves state-of-the-art action following on AgiBot by using offline geometric calibration and counterfactual post-training. It significantly reduced human-assessed interaction defect rates from 48.12% to 6.25% and ranks first in the AgiBot World Challenge 2026 world model track. The model uses a human-annotated video dataset to train an embodied video reward model for feedback without paired ground-truth futures.

Key points

• DreamTrue uses offline geometric calibration to render action trajectories into image-space conditions, aligning them with target videos to improve action following across different robot embodiments.
• Counterfactual post-training modifies recorded action trajectories to generate future videos under a wider range of actions and contact configurations, broadening interaction coverage beyond successful outcomes.

Context

This research addresses two main obstacles in training robot world models: imprecise calibration affecting action following and limited coverage of unsuccessful interactions biasing predictions. The findings are critical for advancing physically plausible video prediction and improving human-robot interaction safety.

Limitations

The model's performance relies on human-annotated video datasets covering defects, and its success on AgiBot does not guarantee results across all robot embodiments or diverse environments. The counterfactual post-training method generates predictions without paired ground-truth futures, introducing potential uncertainty in feedback accuracy.

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