Applications of Reinforcement Learning for Autonomous Surgical Robotics: A Systematic Review.

Autonomous surgical robots are a key new area in medical mechatronics. They aim to make surgeries more precise, reduce surgeon fatigue, and improve patient outcomes. This work reviews and analyzes how reinforcement‑learning (RL) methods are used for autonomous surgical tasks. It looks at several RL approaches—deep RL, inverse RL, and safe RL—and shows how they can model complex actions such as soft‑tissue handling, automated suturing, and dynamic path planning. The study also tackles the main challenges in this field. These include learning efficiently from limited data, dealing with high‑dimensional state spaces, transferring skills from simulation to real robots, and meeting strict safety requirements when humans and robots work together. By

examining both simulation tools and physical robot platforms, the project outlines the current state of the art, points out gaps that hinder clinical use, and suggests clear methods for testing RL‑based surgical controllers. The resulting guide helps engineers design robust, adaptable, and safe autonomous surgical systems, making it easier to move from virtual tests to real‑world clinical applications.

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