Fleurrr/Prophet-World-Model

Prophet World Model

Built on NVIDIA Cosmos.

Prophet is the single-view, history-aware, action-conditioned robot world model introduced in Reinforcing Action Policies by Prophesying. It predicts future robot-observation videos from an initial observation, a 20-step action chunk, and an optional history buffer and projected action-frame sequence.

Released Variants

• prophet-pretrained.pt: heterogeneous robot-data pretrained checkpoint used for downstream adaptation.
• prophet-bridge.pt: full-parameter BRIDGE-adapted checkpoint.
• prophet-libero.pt: full-parameter LIBERO-adapted checkpoint.

Exact sizes and SHA-256 checksums are listed in weights.json.

Internal run identifiers, training dates, and exact checkpoint iterations are intentionally omitted from the public release metadata.

Base Model and License

The checkpoints are derivative models of nvidia/Cosmos-Predict2-2B-Video2World. They are governed by the NVIDIA Open Model License included in NVIDIA_OPEN_MODEL_LICENSE.md. The repository source code is separately available under Apache-2.0.

Licensed by NVIDIA Corporation under the NVIDIA Open Model License.

Intended Use

• Research on action-conditioned robot world models.
• Open-loop or autoregressive video rollout generation.
• Initialization for BRIDGE, LIBERO, or similarly formatted robot-data adaptation.
• Learned-simulator research with independent real-environment validation.

Limitations

• Generated rollouts can hallucinate geometry, contacts, object state, or task success.
• Long autoregressive rollouts accumulate visual and dynamics errors.
• The model is not a replacement for real-robot safety validation or a certified simulator.
• Dataset-specific camera calibration and action semantics affect transfer quality.
• The released checkpoints are single-view; A2World multi-view models are not included.

Citation

@article{zhang2025prophrl,
    title={Reinforcing Action Policies by Prophesying},
    author={Zhang, Jiahui and Huang, Ze and Gu, Chun and Ma, Zipei and Zhang, Li},
    year={2025},
    journal={arXiv preprint arXiv:2511.20633},
}