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: heterogeneous robot-data pretrained A2World checkpoint.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
@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},
}