SSP: Safety-guaranteed Surgical Policy via Joint Optimization of Behavioral and Spatial Constraints

2026-03-07

AI Summary

This work addresses the critical gap in formal safety guarantees for data-driven surgical robot control policies, which hinders their clinical deployment. The authors propose a novel safety-critical control framework that integrates behavioral and spatial constraints by combining an uncertainty-aware Neural ODE dynamics model with Control Barrier Functions (CBFs) to minimally modify black-box policies while ensuring safety. By jointly optimizing CBFs with Control Lyapunov Functions (CLFs), the approach achieves near-zero constraint violation rates without compromising task performance. Validated on both the SurRoL simulation environment and the da Vinci Research Kit, the method demonstrates high task success alongside rigorous safety enforcement, establishing a new paradigm for reliable autonomous surgical systems.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Natural Language Processing: Safety and Robustness
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

The paradigm of robot-assisted surgery is shifting toward data-driven autonomy, where policies learned via Reinforcement Learning (RL) or Imitation Learning (IL) enable the execution of complex tasks. However, these ``black-box"policies often lack formal safety guarantees, a critical requirement for clinical deployment. In this paper, we propose the Safety-guaranteed Surgical Policy (SSP) framework to bridge the gap between data-driven generality and formal safety. We utilize Neural Ordinary Differential Equations (Neural ODEs) to learn an uncertainty-aware dynamics model from demonstration data. This learned model underpins a robust Control Barrier Function (CBF) safety controller, which minimally alters the actions of a surgical policy to ensure strict safety under uncertainty. Our controller enforces two constraint categories: behavioral constraints (restricting the task space of the agent) and spatial constraints (defining surgical no-go zones). We instantiate the SSP framework with surgical policies derived from RL, IL and Control Lyapunov Functions (CLF). Validation on in both the SurRoL simulation and da Vinci Research Kit (dVRK) demonstrates that our method achieves a near-zero constraint violation rate while maintaining high task success rates compared to unconstrained baselines.

Problem

• robot-assisted surgery
• safety guarantees
• data-driven policies
• constraint violation
• clinical deployment

Innovation

• Safety-guaranteed Surgical Policy
• Neural ODEs
• Control Barrier Function
• Behavioral and Spatial Constraints
• Uncertainty-aware Dynamics