| --- |
| license: other |
| license_name: nvidia-open-model-license |
| base_model: nvidia/Cosmos-Predict2-2B-Video2World |
| library_name: cosmos |
| pipeline_tag: image-to-video |
| tags: |
| - robotics |
| - world-model |
| - multi-view-video |
| - action-conditioned-video |
| - cosmos |
| --- |
| |
| # A2World World Model |
|
|
| A2World is a multi-view action-conditioned diffusion world model trained to predict future robot observations from initial camera observations and a 20-step action chunk. A2World-sim adds pose-guided visual history and autoregressive rollout support. |
|
|
| ## Variants |
|
|
| - [`a2world-pretrained.pt`](https://huggingface.co/Fleurrr/A2World-World-Model/resolve/main/a2world-pretrained.pt): heterogeneous robot-data pretrained A2World checkpoint. |
| - [`a2world-libero.pt`](https://huggingface.co/Fleurrr/A2World-World-Model/resolve/main/a2world-libero.pt): history-aware A2World-sim checkpoint adapted on LIBERO. |
|
|
| Both released files use EMA weights, the standard `net.*` inference prefix, and BF16 storage compatible with the public rollout pipeline. |
|
|
| ## Base Model and License |
|
|
| The checkpoints are derivative models of `nvidia/Cosmos-Predict2-2B-Video2World` and are governed by the NVIDIA Open Model License included in this repository. Source code is separately available under Apache-2.0. |
|
|
| ## Intended Use |
|
|
| - Multi-view action-conditioned robot video prediction. |
| - Open-loop and chunk-wise autoregressive world-model rollouts. |
| - LIBERO learned-simulator research and downstream adaptation. |
| - Research on transferable robot dynamics priors. |
|
|
| ## Limitations |
|
|
| - Generated rollouts may hallucinate geometry, contact, object state, or task success. |
| - Autoregressive errors accumulate over long horizons. |
| - Camera identity and action semantics must match the selected embeddings and preprocessing. |
| - The model is not a certified simulator and must not replace real-robot safety validation. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{huang2026a2world, |
| title={Learning Transferable Dynamics Priors from Action to World Modeling}, |
| author={Huang, Ze and Zhang, Jiahui and Liu, Hairuo and Zhang, Chenxi and Cheng, Ran and Zhang, Li}, |
| booktitle={Proceedings of the European Conference on Computer Vision (ECCV)}, |
| year={2026}, |
| } |
| ``` |
|
|
