SSP: Safety-guaranteed Surgical Policy via Joint Optimization of Behavioral and Spatial Constraints
This work is significant for surgical robotics, providing a method to ensure safety for data-driven policies, which is a critical barrier to clinical deployment.
The paper addresses the lack of formal safety guarantees in data-driven robot-assisted surgery policies by proposing the Safety-guaranteed Surgical Policy (SSP) framework. This framework uses Neural ODEs to learn an uncertainty-aware dynamics model, which then informs a robust Control Barrier Function (CBF) safety controller. The method achieved a near-zero constraint violation rate while maintaining high task success rates in both simulation and hardware validation.
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.