Learning Optimal Strategies for Needle Handover in Surgical Suturing

Abstract

Automation of suturing subtasks, such as needle handover, has the potential to reduce surgeons' fatigue and improve surgical efficiency. Needle handover is particularly challenging due to the combinatorial nature of grasping and handover strategies, uncertainties in needle pose estimation, and inaccuracies inherent in cable-driven surgical robots such as the da Vinci system. In this work, we present a reinforcement learning framework for needle handover, spanning the process from initial pickup to a desired grasping state. We formulate the task as a goal-oriented planning problem and design a state–action representation that captures grasping and handover configurations. A DQN-based policy is trained with disturbances that reflect real-world kinematic errors to ensure robustness. The learned policy was validated on the da Vinci Research Kit (dVRK) and quantitatively compared with human teleoperation. Results demonstrate that our approach achieves human-level efficiency in terms of handover attempts (1.65 ± 0.50 vs. 1.62 ± 0.55), while improving consistency and joint-limit avoidance. The proposed framework demonstrates the potential of reinforcement learning for safe and reliable automation of surgical handover and points to opportunities for extending autonomy to more complex handover scenarios.