Interactive World Simulator for Robot Policy Training and Evaluation
Action-conditioned video prediction models (often referred to as world models) have shown strong potential for robotics applications, but existing approaches are often slow and struggle to capture physically consistent interactions over long horizons, limiting their usefulness for scalable robot policy training and evaluation. We present Interactive World Simulator, a framework for building interactive world models from a moderate-sized robot interaction dataset. Our approach leverages consistency models for both image decoding and latent-space dynamics prediction, enabling fast and stable simulation of physical interactions. In our experiments, the learned world models produce interactio...
| Authors | Yixuan Wang, Rhythm Syed, Fangyu Wu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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