Radioguided surgery, such as sentinel lymph node biopsy, relies on the precise localization of radioactive targets by non-imaging gamma/beta detectors. Manual radioactive target detection based on visual display or audible indication of gamma level is highly dependent on the ability of the surgeon to track and interpret the spatial information. This paper presents a learning-based method to realize the autonomous radiotracer detection in robot-assisted surgeries by navigating the probe to the radioactive target. We proposed novel hybrid approach that combines deep reinforcement learning (DRL) with adaptive robotic scanning. The adaptive grid-based scanning could provide initial direction estimation while the DRL-based agent could efficiently navigate to the target utilising historical data. Simulation experiments demonstrate a 95% success rate, and improved efficiency and robustness compared to conventional techniques. Real-world evaluation on the da Vinci Research Kit (dVRK) further confirms the feasibility of the approach, achieving an 80% success rate in radiotracer detection. This method has the potential to enhance consistency, reduce operator dependency, and improve procedural accuracy in radioguided surgeries.

While surgical robots, such as the da Vinci Surgical System, have become prevalent in minimally invasive surgery, they are predominantly used by the human operator to directly teleoperate the tools. This paper aims to analyse the different methods of human robot shared control in the surgical domain. We propose a reinforcement learning algorithm, transverse generative adversarial imitation learning (tGAIL), which is employed to train the robot from the expert’s demonstration and show competitive generalization ability compared to inverse reinforcement learning and conventional GAIL. We then propose a priority-changing shared control method to effectively combine the surgeon and robot’s strengths by dynamically adjusting control priority based on the deviation distance. We show that using this method in a supervision framework boosts the performance of the human operator when completing the peg transfer task. By learning from the expert and collaborating with the human during the task, the intelligent agent can help to reduce operation time by 31.7% and the human input by 60.5% compared to direct teleoperation.

The utilization of shared control in the realm of surgical robotics augments precision and safety by amalgamating human expertise with autonomous assistance. This paper proposes a user-centered shared control framework enabling a robot to learn from expert demonstration, predict operators’ intent and modulate control authority to provide natural assistance when needed. We employ deep inverse reinforcement learning (IRL) to enable the robot to learn path planning from expert demonstrations with fast convergence, subsequently enhancing the policy with a potential field method. The control authority is allocated seamlessly between the human operator and the autonomous agent based on the prediction of operators’ movement from an adaptive filter and fuzzy logic inference. The proposed method is executed using the da Vinci Research Kit (dVRK) robot in a simulation environment, and its effectiveness is assessed through user performance evaluation in a trajectory tracking task. Compared to direct control and simple shared control, the proposed shared control scheme exhibits superior tracking accuracy and trajectory smoothness under external disturbances. Subjective responses underscore users’ perception of the method’s efficacy in enhancing their performance.