发表机构
Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences; Zhejiang Gongshang University（中国科学院自动化研究所; 中国科学院大学人工智能学院; 浙江工商大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对精密医疗机器人中VLA模型连续动作生成不适配的问题，提出分层框架MedVLA，结合高层推理与功能约束执行，在100次电极植入试验中达95%成功率，远超基线。
AI 中文摘要
精密医疗机器人技术要求在严格的安全性、可解释性和执行约束下进行自适应决策。尽管最近的视觉-语言-动作（VLA）模型展现出强大的多模态推理能力，但其连续动作生成范式并不适合精密医疗任务，在这些任务中，可靠的闭环操作可能还依赖于非动作的系统功能调用。为解决这一差距，我们提出了MedVLA，一个将高层多模态推理与低层功能约束执行相结合的分层框架。我们进一步引入了一个可扩展的多智能体流水线，用于生成面向技能思维链（CoT）数据以进行结构化训练。基于不同的多模态大模型骨干，MedVLA在微调后持续提升性能，证明了所提出框架在不同模型变体上的有效性。在相同初始条件下，我们执行了100次闭环柔性电极植入试验。结果表明，MedVLA实现了95.0%的任务成功率，在准确性、稳定性和安全性方面显著优于代表性的VLA基线，包括OpenVLA（8%）和π0（15%）。这些结果表明，带有约束功能级执行的结构化推理是实现可部署精密医疗机器人技术的实用途径。
英文摘要
Precision medical robotics demands adaptive decision-making under strict safety, interpretability, and execution constraints. Although recent Vision-Language-Action (VLA) models show strong multimodal reasoning ability, their continuous action generation paradigm is not well suited for precision medical tasks, where reliable closed-loop operation may also depend on non-action system function calls. To address this gap, we propose MedVLA, a hierarchical framework that couples high-level multimodal reasoning with low-level function-constrained execution. We further introduce a scalable multi-agent pipeline to generate skill-oriented chain-of-thought(CoT) data for structured training. Built on different multimodal large-model backbones, MedVLA consistently improves performance after fine-tuning, demonstrating the effectiveness of the proposed framework across model variants. Under identical initial conditions, we perform 100 closed-loop flexible electrode implantation trials. The results show that MedVLA achieves a 95.0\% task success rate, substantially outperforming representative VLA baselines, including OpenVLA (8\%) and $π_0$ (15\%), in accuracy, stability, and safety. These results indicate that structured reasoning with constrained function-level execution is a practical route toward deployable precision medical robotics.
