robbyant/lingbot-va-base

Causal World Modeling for Robot Control

LingBot-VA has focused on:

• Autoregressive Video-Action World Modeling: Architecturally unifies visual dynamics prediction and action inference within a single interleaved sequence while maintaining their conceptual distinction.
• High-efficiency Execution: A dual-stream mixture-of-transformers(MoT) architecture with Asynchronous Execution and KV Cache.
• Long-Horizon Performance and Generalization: High improvements in sample efficiency, long-horizon success rates, and generalization to novel scenes.

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Model Sources

• Repository: https://github.com/Robbyant/lingbot-va
• Paper: Causal World Modeling for Robot Control
• Project Page: https://technology.robbyant.com/lingbot-va

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📦 Model Download

• Pretrained Checkpoints for Post-Training

Model Name	Huggingface Repository	ModelScope Repository	Description
lingbot-va-base	🤗 robbyant/lingbot-va-base	🤖 Robbyant/lingbot-va-base	LingBot-VA w/ shared backbone
lingbot-va-posttrain-robotwin	🤗 robbyant/lingbot-va-posttrain-robotwin	🤖 Robbyant/lingbot-va-posttrain-robotwin	LingBot-VA-Posttrain-Robotwin w/ shared backbone

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📚Citation

@article{lingbot-va2026,
  title={Causal World Modeling for Robot Control},
  author={Li, Lin and Zhang, Qihang and Luo, Yiming and Yang, Shuai and Wang, Ruilin and Han, Fei and Yu, Mingrui and Gao, Zelin and Xue, Nan and Zhu, Xing and Shen, Yujun and Xu, Yinghao},
  journal={arXiv preprint arXiv:[xxxx]},
  year={2026}
}

🪪 License

This project is released under the Apache License 2.0. See LICENSE file for details.

🧩 Acknowledgments

This work builds upon several excellent open-source projects:

• Wan-Video - Vision transformer backbone
• MoT - Mixture-of-Transformers architecture
• The broader open-source computer vision and robotics communities

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For questions, discussions, or collaborations: