Levels of Autonomy in Robot-Assisted Laparoscopic Surgery: Current Capabilities and Evolutionary Pathways
TL;DR
The review concludes with the main barriers to clinical translation, including the sim-to-real gap, deformable tissue modeling, and safety and regulatory requirements, which show how embodied intelligence may influence future autonomous surgery in the operating room.
Abstract
Robotic laparoscopic surgery is moving from conventional teleoperation toward greater autonomy. Previous studies usually only examined individual parts of this transition. The technologies enabling autonomous laparoscopic surgery are less often discussed as one integrated system. This review examines the system through three components: hardware infrastructure, environmental perception, and decision-making. The hardware discussion covers instrument actuation, vision and force sensing, and various robotic platforms. The perception section follows proprioceptive, visual and force data as they are converted into a unified representation of the surgical state. It focuses on 3D scene reconstruction, instrument segmentation, workflow recognition, and multimodal fusion. The decision and control section discusses classical control and data-driven methods, especially the hierarchical imitation learning and reinforcement learning for long-horizon surgical tasks. The review concludes with the main barriers to clinical translation, including the sim-to-real gap, deformable tissue modeling, and safety and regulatory requirements. These areas together show how embodied intelligence may influence future autonomous surgery in the operating room.