WorldLine: 10k-hour video-trained action simulator lifts robot policy success up to 21.4 pts

hongkongust · hf · 2026-09-30

WorldLine is an action-driven visual simulator that predicts manipulation outcomes before physical execution.

• Decouples transferable dynamics learning from heterogeneous action grounding: 10,000+ hours of action-free robot video for dynamics, 2,000+ hours of trajectories across 10+ embodiments for grounding
• Image-space action representation gives a shared control interface across embodiments; multi-view, failure-enriched training and few-step distillation improve interaction-sensitive causal rollout
• Improves robot-mask IoU by 0.1626 on failed trajectories; predicts trajectory success at 74% mean accuracy across RoboTwin and AgiBot
• Without RoboTwin training or adaptation, its rollouts improve task success by up to 21.4 percentage points over direct policy execution

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