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README.md
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# SemanticVLA · SimplerEnv (WidowX)
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[SemanticVLA](https://github.com/Fei-Ni/SemanticVLA_Offcial) policy trained on BridgeData V2 (Open X-Embodiment `bridge_orig`) for **100K steps**, intended for [SimplerEnv](https://github.com/simpler-env/SimplerEnv) WidowX evaluation. The unified OXE LAM is used as the latent-action tokenizer, and the trace + latent-action auxiliary heads are supervised in the VLM's language stream.
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# SemanticVLA · SimplerEnv (WidowX)
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> 🎉 **Accepted to [CVPR 2026](https://cvpr.thecvf.com/virtual/2026/poster/39352).**
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> ✍️ Fei Ni¹, Zhuo Chen², Yifu Yuan³, Zibin Dong³, Xianze Yao³, Shan Luo², Jianye Hao³, Jiankang Deng¹†, Stefanos Zafeiriou¹† 🏫 ¹Imperial College London ²King's College London ³Tianjin University ✉️ [f.ni@imperial.ac.uk](mailto:f.ni@imperial.ac.uk)
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[SemanticVLA](https://github.com/Fei-Ni/SemanticVLA_Offcial) policy trained on BridgeData V2 (Open X-Embodiment `bridge_orig`) for **100K steps**, intended for [SimplerEnv](https://github.com/simpler-env/SimplerEnv) WidowX evaluation. The unified OXE LAM is used as the latent-action tokenizer, and the trace + latent-action auxiliary heads are supervised in the VLM's language stream.
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