Deliberation in autonomous robotic surgery: a framework for handling anatomical uncertainty

TL;DR

This paper presents DEFRAS, a logic-based framework for autonomous robotic surgery that adapts to anatomical uncertainties by monitoring and updating pre-operative models, improving success rates and safety.

Contribution

It introduces a novel deliberative framework combining planning, monitoring, and learning for autonomous surgical tasks under uncertainty.

Findings

Improved success rate over state-of-the-art methods.

Reduced tissue interaction to prevent damage.

Validated in both simulated and real environments.

Abstract

Autonomous robotic surgery requires deliberation, i.e. the ability to plan and execute a task adapting to uncertain and dynamic environments. Uncertainty in the surgical domain is mainly related to the partial pre-operative knowledge about patient-specific anatomical properties. In this paper, we introduce a logic-based framework for surgical tasks with deliberative functions of monitoring and learning. The DEliberative Framework for Robot-Assisted Surgery (DEFRAS) estimates a pre-operative patient-specific plan, and executes it while continuously measuring the applied force obtained from a biomechanical pre-operative model. Monitoring module compares this model with the actual situation reconstructed from sensors. In case of significant mismatch, the learning module is invoked to update the model, thus improving the estimate of the exerted force. DEFRAS is validated both in simulated and real environment with da Vinci Research Kit executing soft tissue retraction. Compared with state-of-the art related works, the success rate of the task is improved while minimizing the interaction with the tissue to prevent unintentional damage.