VPTwin: Real-Sim-Real Video Prediction for Robotic Manipulation Planning

Abstract:While action-conditioned video prediction provides an intuitive world model for robotics, purely data-driven predictors often suffer from compounding errors and physically implausible hallucinations in long-horizon rollouts, severely undermining downstream action planning. We propose VPTwin, a Real-Sim-Real video prediction framework that anchors real-world future prediction using real-synchronized simulation twins. For a target manipulation task, a VLM reconstructs an executable digital twin from a real demonstration episode. To accommodate the ill-posed estimation of unobserved physical properties, Isaac Sim simulates multiple forward dynamic rollouts across randomized physical configurations under candidate action trajectories. Using these rollouts as in-context references, VPTwin harmonizes both domains, using simulation dynamics to enforce physical plausibility while capturing unmodeled contact interactions from real video. Furthermore, we establish a predictive planning loop using VPTwin to visually verify VLM-proposed actions and guide reliable real-world execution. Evaluations show substantial reductions in physical hallucinations during video prediction and marked improvements in manipulation planning performance.

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2609.33104 [cs.RO]

(or arXiv:2609.33104v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.33104 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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