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D2E: Scaling Vision-Action Pretraining on Desktop Data for Transfer to Embodied AI

arXiv – CS AI|Suhwan Choi, Jaeyoon Jung, Haebin Seong, Minchan Kim, Minyeong Kim, Yongjun Cho, Yoonshik Kim, Yubeen Park, Youngjae Yu, Yunsung Lee|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers developed D2E (Desktop to Embodied AI), a framework that uses desktop gaming data to pretrain AI models for robotics tasks. Their 1B-parameter model achieved 96.6% success on manipulation tasks and 83.3% on navigation, matching performance of models up to 7 times larger while using scalable desktop data instead of expensive physical robot training data.

Key Takeaways

→D2E framework successfully transfers learning from desktop gaming environments to real-world robotics tasks.
→The approach uses 1.3K+ hours of desktop data with 152x compression through the OWA Toolkit.
→A 1B-parameter model matches performance of much larger models like OpenVLA (7B parameters).
→The framework includes public datasets and tools, unlike previous proprietary approaches.
→Desktop pretraining offers a cost-effective alternative to expensive physical robot data collection.

#embodied-ai #robotics #pretraining #desktop-data #transfer-learning #gaming-ai #machine-learning #computer-vision #open-source

Read Original →via arXiv – CS AI

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