arXiv 2511.141782026-04-16cs.ROcs.AI

迈向无需微调的VLA部署：通过具身进化扩散实现即插即用的推理时VLA策略引导

Towards Deploying VLA without Fine-Tuning: Plug-and-Play Inference-Time VLA Policy Steering via Embodied Evolutionary Diffusion

Collaborative and Versatile Robots (CLOVER) Laboratory, T-Stone Robotics Institute, The Chinese University of Hong Kong, Hong Kong（协作与多功能机器人实验室，T-Stone机器人研究所，香港中文大学，香港）；Φ \Phi -Institute for Physical Human Intelligence（物理人机智能研究所）；Center for Embodied Artificial Intelligence and Computer Vision, Shenzhen Loop Area Institute, Shenzhen, China（具身人工智能与计算机视觉中心，深圳环园研究院，深圳，中国）；Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy（先进机器人系，意大利理工学院，热那亚，意大利）

Zhuo Li, Junjia Liu, Zhipeng Dong, Tao Teng, Quentin Rouxel, Darwin Caldwell, Fei Chen

备注：: 9 pages, 8 figures, submitted to IEEE RA-L

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AI总结：

本文提出VLA-Pilot方法，通过即插即用的策略引导实现无需微调的零样本部署，提升预训练VLA在不同任务和机器人平台上的表现。

摘要：

视觉-语言-动作（VLA）模型在现实世界机器人操作中展现出显著潜力。然而，预训练VLA策略在下游部署中仍存在显著性能下降。尽管微调可以缓解这一问题，但其依赖于昂贵的演示收集和高强度计算，使其在现实场景中不切实际。本文引入VLA-Pilot，一种即插即用的推理时策略引导方法，用于无需任何额外微调或数据收集的零样本部署。我们评估VLA-Pilot在六个现实世界下游操作任务上，涵盖两种不同的机器人具身情况，包括在分布和非在分布场景。实验结果表明，VLA-Pilot显著提升了现成预训练VLA策略的成功率，使其能够稳健地实现对多样化任务和具身的零样本泛化。实验视频和代码可在：https://rip4kobe.github.io/vla-pilot/ 上获取。

英文摘要：

Vision-Language-Action (VLA) models have demonstrated significant potential in real-world robotic manipulation. However, pre-trained VLA policies still suffer from substantial performance degradation during downstream deployment. Although fine-tuning can mitigate this issue, its reliance on costly demonstration collection and intensive computation makes it impractical in real-world settings. In this work, we introduce VLA-Pilot, a plug-and-play inference-time policy steering method for zero-shot deployment of pre-trained VLA without any additional fine-tuning or data collection. We evaluate VLA-Pilot on six real-world downstream manipulation tasks across two distinct robotic embodiments, encompassing both in-distribution and out-of-distribution scenarios. Experimental results demonstrate that VLA-Pilot substantially boosts the success rates of off-the-shelf pre-trained VLA policies, enabling robust zero-shot generalization to diverse tasks and embodiments. Experimental videos and code are available at: https://rip4kobe.github.io/vla-pilot/.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。