RL-MACRO: A Cybernetic Closed-Loop Intelligence Framework for Multimodal Adaptive Robotic Craniotomy

2026-07-23

AI Summary

This study addresses the risks of mechanical overload and thermal injury in autonomous robotic craniotomy, which arise from unknown tissue properties and unmeasurable temperature. To mitigate these challenges, the authors propose a closed-loop intelligent framework integrating multimodal perception, adaptive decision-making, and precise execution. The framework employs a CNN-LSTM observer to reconstruct unmeasurable temperature states from partial sensor data and leverages offline Implicit Q-Learning (IQL) to design a dual-headed actor policy that jointly optimizes feed rate, spindle speed, and cutting depth under safety constraints. Additionally, it incorporates online trajectory replanning and velocity servo control. Experimental results demonstrate high-fidelity temperature reconstruction (R² = 0.939, MAE = 1.717°C) on bovine ribs and goat skulls, ensuring stable cutting over complex curved surfaces and robust adaptive recovery from force or thermal anomalies.

Abstract

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.

Problem

• autonomous robotic craniotomy
• partially observable environment
• tool-tissue interaction
• thermal damage
• mechanical overload

Innovation

• multimodal perception
• closed-loop control
• implicit Q-learning
• temperature reconstruction
• adaptive robotic surgery