9.5亿参数模型实现高效通用机器人控制，性能媲美大模型。
FLOWER: Democratizing Generalist Robot Policies with Efficient Vision-Language-Action Flow Policies
- 通过剪枝语言模型层和模块化条件调节，提升效率
- 200小时训练完成，在190个任务中表现优于大模型
- 适合资源有限但需通用机器人控制的研究者
开发高效的视觉-语言-动作（VLA）策略对实际机器人部署至关重要，但现有方法计算成本高、资源需求大。当前基于扩散的VLA策略需数十亿参数模型和海量数据才能达到良好性能。本文提出两项改进：中间模态融合，通过剪除高达50%的LLM层，将算力分配给扩散头；动作特定全局自适应归一化条件，通过模块化适配减少20%参数量。结合这两项技术，我们构建了新型9.5亿参数的VLA模型FLOWER。仅用200小时H100 GPU训练，其在涵盖十种仿真与真实世界基准的190个任务中表现优异，且对多种机器人形态具有鲁棒性。此外，其在CALVIN ABC基准上取得4.53的新最佳成绩。演示、代码及预训练权重可在https://intuitive-robots.github.io/flower_vla/获取。
原文摘要 · Abstract (English)
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/.
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