FLOWER: Democratizing Generalist Robot Policies with Efficient
  Vision-Language-Action Flow Policies
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/.
68c7c757ee0eed1697d6b809
Moritz Reuss
68c7c757ee0eed1697d6b80a
Hongyi Zhou
68c7c757ee0eed1697d6b80b
Marcel Rühle
68c7c757ee0eed1697d6b80c
Ömer Erdinç Yağmurlu
68c7c757ee0eed1697d6b80d
Fabian Otto
68c7c757ee0eed1697d6b80e
Rudolf Lioutikov
FLOWER: Democratizing Generalist Robot Policies with Efficient
  Vision-Language-Action Flow Policies
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/.
FLOWER, a 950 M-parameter VLA policy, achieves competitive performance with reduced computational costs through intermediate-modality fusion and action-specific Global-AdaLN conditioning.
diffusion-based VLA policies intermediate-modality fusion diffusion head LLM layers action-specific Global-AdaLN conditioning FLOWER CALVIN ABC benchmark