RynnVLA-002: A Unified Vision-Language-Action and World Model
We introduce RynnVLA-002, a unified Vision-Language-Action (VLA) and world model. The world model leverages action and visual inputs to predict future image states, learning the underlying physics of the environment to refine action generation. Conversely, the VLA model produces subsequent actions from image observations, enhancing visual understanding and supporting the world model's image generation. The unified framework of RynnVLA-002 enables joint learning of environmental dynamics and action planning. Our experiments show that RynnVLA-002 surpasses individual VLA and world models, demonstrating their mutual enhancement. We evaluate RynnVLA-002 in both simulation and real-world robot tasks. RynnVLA-002 achieves 97.4% success rate on the LIBERO simulation benchmark without pretraining, while in real-world LeRobot experiments, its integrated world model boosts the overall success rate by 50%.
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Jun Cen
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Siteng Huang
admin_assigned
2025-11-24T12:27:31.337Z
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Siteng Huang
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user
huangsiteng
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Yuqian Yuan
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Hangjie Yuan
admin_assigned
2025-11-24T12:27:45.960Z
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Hangjie Yuan
JacobYuan
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JacobYuan
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Chaohui Yu
admin_assigned
2025-11-24T12:27:52.601Z
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CHAO-HUI-YU
Alex-snow
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Alex-snow
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Yuming Jiang
admin_assigned
2025-11-24T12:27:59.203Z
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Yuming Jiang
yumingj
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Jiayan Guo
admin_assigned
2025-11-24T12:28:06.395Z
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Jiayan Guo
SpaceProduct
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SpaceProduct
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Kehan Li
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Hao Luo
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Fan Wang
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Xin Li
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Deli Zhao
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Hao Chen
RynnVLA-002: A Unified Vision-Language-Action and World Model
We introduce RynnVLA-002, a unified Vision-Language-Action (VLA) and world model. The world model leverages action and visual inputs to predict future image states, learning the underlying physics of the environment to refine action generation. Conversely, the VLA model produces subsequent actions from image observations, enhancing visual understanding and supporting the world model's image generation. The unified framework of RynnVLA-002 enables joint learning of environmental dynamics and action planning. Our experiments show that RynnVLA-002 surpasses individual VLA and world models, demonstrating their mutual enhancement. We evaluate RynnVLA-002 in both simulation and real-world robot tasks. RynnVLA-002 achieves 97.4% success rate on the LIBERO simulation benchmark without pretraining, while in real-world LeRobot experiments, its integrated world model boosts the overall success rate by 50%.
A unified Vision-Language-Action (VLA) and world model, RynnVLA-002, jointly learns environmental dynamics and action planning, outperforming individual models in both simulation and real-world tasks.
Vision-Language-Action (VLA) world model image states underlying physics action generation visual understanding environmental dynamics action planning LIBERO simulation benchmark LeRobot experiments