| Field | Response | |
| :------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------- | |
| Intended Task/Domain: | Vision-to-action model designed to play video games directly from raw frames | |
| Model Type: | Transformer | |
| Intended Users: | Researchers, game developers, open source community, gamers. Potential applications include next-generation game AI, automating testing for video games, and generally advancing research in embodied AI. | |
| Output: | Gamepad actions | |
| Describe how the model works: | Image inputs are encoded with a vision transformer. A separate diffusion transformer is conditioned on the image embeddings, which then denoise an action tensor | |
| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable | |
| Technical Limitations & Mitigation: | This model performs well on games played with a gamepad. Model may not perform well on games played with a keyboard or mouse. | |
| Verified to have met prescribed NVIDIA quality standards: | Yes | |
| Performance Metrics: | Task success rate | |
| Potential Known Risks: | The model may occasionally lose at certain games. | |
| Licensing: | Governing Terms: [NVIDIA License](https://developer.download.nvidia.com/licenses/NVIDIA-OneWay-Noncommercial-License-22Mar2022.pdf). Additional Information: [Apache License](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) for [https://huggingface.co/google/siglip2-base-patch16-224](). | |