Shenghe Zheng

WorldLine introduces an action-driven visual simulator that decouples transferable robot-object dynamics learning from heterogeneous action grounding: it pretrains on more than 10,000 hours of action-free robot videos, grounds them with over 2,000 hours of action trajectories across more than ten embodiments through image-space action maps, adds multi-view, failure-enriched and relational-regularized training, and distills a robot-focused few-step causal rollout; across held-out and out-of-domain settings it maintains visual quality and robot-motion agreement, improves robot-mask IoU by 0.1626 over the strongest baseline on failed trajectories, predicts trajectory success with 74% mean accuracy on RoboTwin and AgiBot, and improves task success by up to 21.

WorldLine introduces an action-driven visual simulator that decouples transferable robot-object dynamics learning from heterogeneous action grounding: it pretrains on more than 10,000 hours of action-free robot videos, grounds them with over 2,000 hours of action trajectories across more than ten embodiments through image-space action maps, adds multi-view, failure-enriched and relational-regularized training, and distills a robot-focused few-step causal rollout; across held-out and out-of-domain settings it maintains visual quality and robot-motion agreement, improves robot-mask IoU by 0.1626 over the strongest baseline on failed trajectories, predicts trajectory success with 74% mean accuracy on RoboTwin and AgiBot, and improves task success by up to 21.