Give Me Scissors: Collision-Free Dual-Arm Surgical Assistive Robot for Instrument Delivery
Xuejin Luo, Shiquan Sun, Runshi Zhang, Ruizhi Zhang, Junchen Wang
AI summary
Problem
Existing robotic scrub nurses rely on predefined paths that limit generalizability and lack real-time collision avoidance, posing safety risks in dynamic surgical settings.
Approach
The system uses a vision-language model to generate zero-shot grasping and delivery trajectories from surgeon instructions, while a real-time quadratic programming framework with neural distance prediction ensures reactive obstacle and self-collision avoidance.
Key results
- Developed a dual-arm surgical assistive robot for zero-shot instrument delivery
- Proposed a real-time QP framework with neural distance prediction for simultaneous obstacle and self-collision avoidance
- Achieved an 83.33% success rate in real-world surgical instrument delivery trials
- Outperformed state-of-the-art reactive avoidance methods in optimization speed, trajectory accuracy, and motion smoothness
Why it matters
Enables safer, more adaptable robotic assistance in operating rooms, reducing scrub nurse fatigue and improving surgical team efficiency.
Abstract
During surgery, scrub nurses are required to frequently deliver surgical instruments to surgeons, which can lead to physical fatigue and decreased focus. Robotic scrub nurses provide a promising solution that can replace repetitive tasks and enhance efficiency. Existing research on robotic scrub nurses relies on predefined paths for instrument delivery, which limits their generalizability and poses safety risks in dynamic environments. To address these challenges, we present a collision-free dual-arm surgical assistive robot capable of performing instrument delivery. A vision-language model is utilized to automatically generate the robot’s grasping and delivery trajectories in a zero-shot manner based on surgeons’ instructions. A real-time obstacle minimum distance perception method is proposed and integrated into a unified quadratic programming framework. This framework ensures reactive obstacle avoidance and self-collision prevention during the dual- arm robot’s autonomous movement in dynamic environments. Extensive experimental validations demonstrate that the pro- posed robotic system achieves an 83.33% success rate in surgical instrument delivery while maintaining smooth, collision-free movement throughout all trials. The project page and source code are available at https://give-me-scissors.github.io/.