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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]().
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