FLOWER: Democratizing Generalist Robot Policies with Efficient Vision-Language-Action Flow Policies
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914023969128448 |
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| author | Reuss, Moritz Zhou, Hongyi Rühle, Marcel Yağmurlu, Ömer Erdinç Otto, Fabian Lioutikov, Rudolf |
| author_facet | Reuss, Moritz Zhou, Hongyi Rühle, Marcel Yağmurlu, Ömer Erdinç Otto, Fabian Lioutikov, Rudolf |
| contents | Developing efficient Vision-Language-Action (VLA) policies is crucial for practical robotics deployment, yet current approaches face prohibitive computational costs and resource requirements. Existing diffusion-based VLA policies require multi-billion-parameter models and massive datasets to achieve strong performance. We tackle this efficiency challenge with two contributions: intermediate-modality fusion, which reallocates capacity to the diffusion head by pruning up to $50\%$ of LLM layers, and action-specific Global-AdaLN conditioning, which cuts parameters by $20\%$ through modular adaptation. We integrate these advances into a novel 950 M-parameter VLA called FLOWER. Pretrained in just 200 H100 GPU hours, FLOWER delivers competitive performance with bigger VLAs across $190$ tasks spanning ten simulation and real-world benchmarks and demonstrates robustness across diverse robotic embodiments. In addition, FLOWER achieves a new SoTA of 4.53 on the CALVIN ABC benchmark. Demos, code and pretrained weights are available at https://intuitive-robots.github.io/flower_vla/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04996 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FLOWER: Democratizing Generalist Robot Policies with Efficient Vision-Language-Action Flow Policies Reuss, Moritz Zhou, Hongyi Rühle, Marcel Yağmurlu, Ömer Erdinç Otto, Fabian Lioutikov, Rudolf Robotics Developing efficient Vision-Language-Action (VLA) policies is crucial for practical robotics deployment, yet current approaches face prohibitive computational costs and resource requirements. Existing diffusion-based VLA policies require multi-billion-parameter models and massive datasets to achieve strong performance. We tackle this efficiency challenge with two contributions: intermediate-modality fusion, which reallocates capacity to the diffusion head by pruning up to $50\%$ of LLM layers, and action-specific Global-AdaLN conditioning, which cuts parameters by $20\%$ through modular adaptation. We integrate these advances into a novel 950 M-parameter VLA called FLOWER. Pretrained in just 200 H100 GPU hours, FLOWER delivers competitive performance with bigger VLAs across $190$ tasks spanning ten simulation and real-world benchmarks and demonstrates robustness across diverse robotic embodiments. In addition, FLOWER achieves a new SoTA of 4.53 on the CALVIN ABC benchmark. Demos, code and pretrained weights are available at https://intuitive-robots.github.io/flower_vla/. |
| title | FLOWER: Democratizing Generalist Robot Policies with Efficient Vision-Language-Action Flow Policies |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.04996 |