SimRetract: Zero-Shot Sim-to-Real Robotic Assistance for Retractor Take-Over in Open Surgery
Overview
This research introduces SimRetract, the first zero-shot sim-to-real transfer system for robotic assistance in open surgery, specifically for retractor take-over. The robot arm learns from simulated demonstrations to grasp retractors from a surgeon's hand while preserving retraction, achieving 90% success on phantom presentations and 78% retention of retraction without real-world fine-tuning or human demonstrations.
Generated from attributed reports · Updated 58 minutes ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · simulated trials count: 1200 other · Basis not reported
“efined phantom presentations and preserves retraction at handoff in 78%. Results on unseen retractors suggest potential for generalization to instruments with similar grasp geometry. Across ten policy configurations and 1,200 simulated and real trials, we localize the sim-to-real gap to components: in simulation, grasp rates across ablations span 80-96%, while on the real robot removing the instrument mask alone drop”
Exact source · revision 2Source owner not reported
Reported quantity · real trials count: 1200 other · Basis not reported
“efined phantom presentations and preserves retraction at handoff in 78%. Results on unseen retractors suggest potential for generalization to instruments with similar grasp geometry. Across ten policy configurations and 1,200 simulated and real trials, we localize the sim-to-real gap to components: in simulation, grasp rates across ablations span 80-96%, while on the real robot removing the instrument mask alone drop”
Exact source · revision 2Source owner not reported
Reported quantity · policy configurations count: 10 other · Basis not reported
“efined phantom presentations and preserves retraction at handoff in 78%. Results on unseen retractors suggest potential for generalization to instruments with similar grasp geometry. Across ten policy configurations and 1,200 simulated and real trials, we localize the sim-to-real gap to components: in simulation, grasp rates across ablations span 80-96%, while on the real robot removing the instrument mask alone drop”
Exact source · revision 2Source owner not reported
Artifact availability · code: available
“s performance to 22% grasped and 12% held. Transfer-oriented design thus makes zero-shot sim-to-real transfer feasible for a surgical assistance task without human demonstrations. Code is available here: https://anonymous.4open.science/r/simretract-58D2.”
Exact source · revision 2Source owner not reported
Evidence dependency changes (1)
- 2026-11-02T19:28:13.894Z · evidence updated · source revision 2. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalSimRetract: Zero-Shot Sim-to-Real Robotic Assistance for Retractor Take-Over in Open Surgery
This research introduces SimRetract, the first zero-shot sim-to-real transfer system for robotic assistance in open surgery, specifically for retractor take-over. The robot arm learns from simulated demonstrations to grasp retractors from a surgeon's hand while preserving retraction, achieving 90% success on phantom presentations and 78% retention of retraction without real-world fine-tuning or human demonstrations.
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