发表机构
Dalian University of Technology; Second Hospital of Dalian Medical University; The University of Tokyo（大连理工大学; 大连医科大学附属第二医院; 东京大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对自主机器人开颅手术面临的挑战，提出RL-MACRO框架，通过多模态感知、自适应决策和机器人执行实现闭环控制，利用CNN-LSTM观测器重建温度，经IQL策略和双头执行器优化行为，实验验证了该框架在骨切割方面的有效性。
AI 中文摘要
自主机器人开颅手术需要持续调节工具与组织的相互作用，以减轻机械过载和热损伤，同时保持手术效率。然而，由于组织特性未知且随时间变化，以及在物理遮挡下无法直接测量切割温度，这个过程本质上是部分可观察的。为应对这些挑战，我们提出了RL-MACRO，一个将多模态感知、自适应决策和机器人执行相结合的控制论闭环智能框架。该框架使手术机器人能够从部分感官反馈中自主感知不可达状态，并在不确定环境中动态优化其行为。一个CNN-LSTM观测器首先融合力和声音反馈来重建隐藏的温度状态。这个重建的温度与多传感器特征一起，形成了离线隐式Q学习（IQL）策略的置信状态。一个新颖的双头执行器动态协调进给速度、主轴速度和切削深度，以在严格的安全范围内优化效率。这些决策通过在线轨迹重新规划和速度伺服无缝转换为空间运动。在牛肋骨和六个离体山羊头骨上的实验验证了该系统强大的感知能力、从力/温度偏移中自适应恢复的能力以及在不规则表面上的平稳执行能力，建立了一个数据驱动的控制论范式，用于安全高效的自主骨切割。
英文摘要
Autonomous robotic craniotomy requires continuous regulation of tool-tissue interactions to mitigate mechanical overload and thermal damage while maintaining surgical efficiency. However, this process is inherently partially observable due to unknown, time-varying tissue properties and the inability to directly measure cutting temperatures under physical occlusion. To address these challenges, we propose RL-MACRO, a cybernetic closed-loop intelligence framework that couples multimodal perception, adaptive decision-making, and robotic execution. This framework empowers the surgical robot to autonomously perceive inaccessible states from partial sensory feedback and dynamically optimize its behaviors under uncertain environment. A CNN-LSTM observer first fuses force and sound feedback to reconstruct the hidden temperature state (R^2=0.939, MAE = 1.717 deg C). This reconstructed temperature, alongside multi-sensor features, forms the belief state for an offline Implicit Q-Learning (IQL) policy. A novel dual-head Actor dynamically coordinates the feed rate, spindle speed, and cutting depth to optimize efficiency within strict safety bounds. These decisions are seamlessly translated into spatial motions via online trajectory re-planning and velocity servoing. Experiments on bovine ribs and six ex vivo goat skulls validate the system's robust perception, adaptive recovery from force/temperature excursions, and smooth execution on irregular surfaces, establishing a data-driven cybernetic paradigm for safe and efficient autonomous bone cutting.
