Safe Uncertainty-Aware Learning Framework for Robotic Suturing

Robot-Assisted Minimally Invasive Surgery is currently fully manually controlled by a trained surgeon. The integration of automation into this process has great potential for alleviating issues, e.g., physical strain, highly repetitive tasks, and shortages of trained surgeons. Accordingly, recent works have utilized Artificial Intelligence methods, which show promising adaptability. Despite these advances, there is skepticism of these methods because they lack explainability and robust safety guarantees. This paper presents a framework for a safe, uncertainty-aware learning method. We train an Ensemble Model of Diffusion Policies using expert demonstrations of needle insertion. Using an Ensemble model, we can quantify the policy’s epistemic uncertainty, which is used to determine Out-Of-Distribution scenarios. This allows the system to release control back to the surgeon in the event of an unsafe scenario. Additionally, we implement a model-free Control Barrier Function to place formal safety guarantees on the predicted action. We experimentally evaluate our proposed framework using a state-of-the-art robotic suturing simulator. We evaluate multiple scenarios, such as dropping the needle, moving the camera, and moving the phantom. The learned policy demonstrates corrective behaviors by recovering from these perturbations–e.g., compensating for a moving suture pad –and it is possible to detect Out-Of-Distribution scenarios. We further demonstrate that the Control Barrier Function successfully limits the action to remain within our specified safety set in the case of unsafe predictions.