Safe Uncertainty-Aware Learning of Robotic Suturing

Robot-Assisted Minimally Invasive Surgery is currently fully manually controlled by a trained surgeon. Automating this has great potential for alleviating issues, e.g., physical strain, highly repetitive tasks, and shortages of trained surgeons. For these reasons, 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 is robust to these perturbations, showing corrective behaviors and generalization, 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.

EndoForce: Development of an Intuitive Axial Force Measurement Device for Endoscopic Robotic Systems

Robotic endoscopic systems provide intuitive control and eliminate radiation exposure, making them a promising alternative to conventional methods. However, the lack of axial force measurement from the robot remains a major challenge, as it can lead to excessive colonic elongation, perforation, or ureteral complications. Although various methods have been proposed in previous studies, limitations such as model dependency, bulkiness, and environmental sensitivity remain challenges that should be addressed before clinical application. In this study, we propose EndoForce, a device designed for intuitive and accurate axial force measurement in endoscopic robotic systems. Inspired by the insertion motion performed by medical doctors during ureteroscopy and gastrointestinal (GI) endoscopy, EndoForce ensures precise force measuring while maintaining compatibility with clinical environments. The device features a streamlined design, allowing for the easy attachment and detachment of a sterile cover, and incorporates a commercial load cell to enhance cost-effectiveness and facilitate practical implementation in real medical applications. To validate the effectiveness of the proposed EndoForce, physical experiments were performed using a testbed that simulates the ureter. We show that the axial force generated during insertion was measured with high accuracy, regardless of whether the pathway was straight or curved, in a testbed simulating the human ureter.

Safety-Ensured Control Framework for Robotic Endoscopic Task Automation

There is growing interest in automating surgical tasks using robotic systems, such as endoscopy for treating gastrointestinal (GI) cancer. However, previous studies have primarily focused on detecting and analyzing objects or robots, with limited attention to ensuring safety, which is critical for clinical applications, where accidents can be caused by unsafe robot motions. In this study, we propose a new control framework that can formally ensure the safety of automating certain processes involved in endoscopic submucosal dissection (ESD), a representative endoscopic surgical method for the treatment of early GI cancer, by using an endoscopic robot. The proposed framework utilizes Control Barrier Functions (CBFs) to accurately identify the boundaries of individual tumors, even in close proximity within the GI tract, ensuring precise treatment and removal while preserving the surrounding normal tissue. Additionally, by adopting a model-free control scheme, safety assurance is made possible even in endoscopic robotic systems where dynamic modeling is challenging. We demonstrate the proposed framework in cases where the tumors to be removed are close to each other, showing that the safety constraints are enforced. We show that the model-free CBF-based controlled robot eliminates one tumor completely without damaging it, while not invading another nearby tumor.

STITCH: Augmented Dexterity for Suture Throws Including Thread Coordination and Handoffs

We present STITCH: an augmented dexterity pipeline that performs Suture Throws Including Thread Coordination and Handoffs. STITCH iteratively performs needle insertion, thread sweeping, needle extraction, suture cinching, needle handover, and needle pose correction with failure recovery policies. We introduce a novel visual 6D needle pose estimation framework using a stereo camera pair and new suturing motion primitives. We compare STITCH to baselines, including a proprioception-only and a policy without visual servoing. In physical experiments across 15 trials, STITCH achieves an average of 2.93 sutures without human intervention and 4.47 sutures with human intervention. See https://sites.google.com/berkeley.edu/stitch for code and supplemental materials.