外观
论文arXiv 2502.196452025-02-27
OpenVLA-OFT微调方案：LIBERO成功率97.1%且推理吞吐提升26倍
本文由 AI 根据公开内容整理，可能有误，请以原文为准：Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success（arXiv 2502.19645）
核心内容
针对视觉-语言-动作模型（VLA）在新机器人平台适配难的问题，研究者以OpenVLA为基座提出优化微调配方OFT。实验显示，该方法在LIBERO仿真基准四个任务套件中平均成功率达97.1%，较原版提升20.6个百分点；同时动作生成吞吐量提升26倍，显著改善了VLA模型的实时控制能力与部署效率。
OFT核心技术整合了并行解码、动作分块（Action Chunking）、连续动作表征及L1回归损失函数。在真实世界双臂ALOHA机器人灵巧高频控制任务中，OpenVLA-OFT不仅优于使用默认配方的π0和RDT-1B模型，还比从零训练的Diffusion Policy和ACT模仿学习策略平均成功率高出15%。相关代码与预训练权重已开源。
关键看点
- LIBERO性能突破：OpenVLA-OFT在LIBERO仿真基准平均成功率达97.1%，较基线提升20.6%，确立新SOTA
- 推理效率优化：集成并行解码与动作分块技术，使VLA模型动作生成吞吐量提升26倍
- 真机泛化验证：在双臂ALOHA机器人高频灵巧操作中，绝对成功率超Diffusion Policy与ACT达15%
- 开源资产释放：完整开源OFT微调代码及预训练模型检查点，支持社区复现与二次开发
工程与产业观察
该工作证明了通用VLA通过系统化微调配方可兼顾高成功率与实时性，解决了大模型落地具身智能的延迟瓶颈。其验证的连续动作表征与L1损失组合，为双臂协作等高频控制场景提供了优于传统模仿学习的标准化迁移范式，降低了专用策略训练成本。
英文摘要 (Abstract)
Recent vision-language-action models (VLAs) build upon pretrained vision-language models and leverage diverse robot datasets to demonstrate strong task execution, language following ability, and semantic generalization. Despite these successes, VLAs struggle with novel robot setups and require fine-tuning to achieve good performance, yet how to most effectively fine-tune them is unclear given many possible strategies. In this work, we study key VLA adaptation design choices such as different action decoding schemes, action representations, and learning objectives for fine-tuning, using OpenVLA as our representative base model. Our empirical analysis informs an Optimized Fine-Tuning (OFT) recipe that integrates parallel decoding, action chunking, a continuous action representation, and a simple L1 regression-based learning objective to altogether improve inference efficiency, policy performance, and flexibility in the model's input-output specifications. We propose OpenVLA-OFT, an instantiation of this recipe, which sets a new state of the art on the LIBERO simulation benchmark, significantly boosting OpenVLA's average success rate across four task suites from 76.5% to 97.1% while increasing action generation throughput by 26$\times$. In real-world evaluations, our fine-tuning recipe enables OpenVLA to successfully execute dexterous, high-frequency control tasks on a bimanual ALOHA robot and outperform other VLAs ($π_0$ and RDT-1B) fine-tuned using their default recipes, as well as strong imitation learning policies trained from scratch (Diffusion Policy and ACT) by up to 15% (absolute) in average success rate. We release code for OFT and pretrained model checkpoints at https://openvla-oft.github.io/.
作者：Moo Jin Kim, Chelsea Finn, Percy Liang
来源
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