Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning

Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons

Deep Reinforcement Learning in Surgical Robotics: Enhancing the Automation Level

Surgical robotics is a rapidly evolving field that is transforming the landscape of surgeries. Surgical robots have been shown to enhance precision, minimize invasiveness, and alleviate surgeon fatigue. One promising area of research in surgical robotics is the use of reinforcement learning to enhance the automation level. Reinforcement learning is a type of machine learning that involves training an agent to make decisions based on rewards and punishments. This literature review aims to comprehensively analyze existing research on reinforcement learning in surgical robotics. The review identified various applications of reinforcement learning in surgical robotics, including pre-operative, intra-body, and percutaneous procedures, listed the typical studies, and compared their methodologies and results. The findings show that reinforcement learning has great potential to improve the autonomy of surgical robots. Reinforcement learning can teach robots to perform complex surgical tasks, such as suturing and tissue manipulation. It can also improve the accuracy and precision of surgical robots, making them more effective at performing surgeries

Context-aware learning for robot-assisted endovascular catheterization

Endovascular intervention has become a mainstream treatment of cardiovascular diseases. However, multiple challenges remain such as unwanted radiation exposures, limited two-dimensional image guidance, insufficient force perception and haptic cues. Fast evolving robot-assisted platforms improve the stability and accuracy of instrument manipulation. The master-slave system also removes radiation to the operator. However, the integration of robotic systems into the current surgical workflow is still debatable since repetitive, easy tasks have little value to be executed by the robotic teleoperation. Current systems offer very low autonomy, potential autonomous features could bring more benefits such as reduced cognitive workloads and human error, safer and more consistent instrument manipulation, ability to incorporate various medical imaging and sensing modalities. This research proposes frameworks for automated catheterisation with different machine learning-based algorithms, includes Learning-from-Demonstration, Reinforcement Learning, and Imitation Learning. Those frameworks focused on integrating context for tasks in the process of skill learning, hence achieving better adaptation to different situations and safer tool-tissue interactions. Furthermore, the autonomous feature was applied to next-generation, MR-safe robotic catheterisation platform. The results provide important insights into improving catheter navigation in the form of autonomous task planning, self-optimization with clinical relevant factors, and motivate the design of intelligent, intuitive, and collaborative robots under non-ionizing image modalities.Open Acces

Constrained Reinforcement Learning and Formal Verification for Safe Colonoscopy Navigation

The field of robotic Flexible Endoscopes (FEs) has progressed significantly, offering a promising solution to reduce patient discomfort. However, the limited autonomy of most robotic FEs results in non-intuitive and challenging manoeuvres, constraining their application in clinical settings. While previous studies have employed lumen tracking for autonomous navigation, they fail to adapt to the presence of obstructions and sharp turns when the endoscope faces the colon wall. In this work, we propose a Deep Reinforcement Learning (DRL)-based navigation strategy that eliminates the need for lumen tracking. However, the use of DRL methods poses safety risks as they do not account for potential hazards associated with the actions taken. To ensure safety, we exploit a Constrained Reinforcement Learning (CRL) method to restrict the policy in a predefined safety regime. Moreover, we present a model selection strategy that utilises Formal Verification (FV) to choose a policy that is entirely safe before deployment. We validate our approach in a virtual colonoscopy environment and report that out of the 300 trained policies, we could identify three policies that are entirely safe. Our work demonstrates that CRL, combined with model selection through FV, can improve the robustness and safety of robotic behaviour in surgical applications.Comment: Accepted in the IEEE International Conference on Intelligent Robots and Systems (IROS), 2023. [Corsi, Marzari and Pore contributed equally

Safe Deep Reinforcement Learning: Enhancing the Reliability of Intelligent Systems

In the last few years, the impressive success of deep reinforcement learning (DRL) agents in a wide variety of applications has led to the adoption of these systems in safety-critical contexts (e.g., autonomous driving, robotics, and medical applications), where expensive hardware and human safety can be involved. In such contexts, an intelligent learning agent must adhere to certain requirements that go beyond the simple accomplishment of the task and typically include constraints on the agent's behavior. Against this background, this thesis proposes a set of training and validation methodologies that constitute a unified pipeline to generate safe and reliable DRL agents. In the first part of this dissertation, we focus on the problem of constrained DRL, leaving the challenging problem of the formal verification of deep neural networks for the second part of this work. As humans, in our growing process, the help of a mentor is crucial to learn effective strategies to solve a problem while a learning process driven only by a trial-and-error approach usually leads to unsafe and inefficient solutions. Similarly, a pure end-to-end deep reinforcement learning approach often results in suboptimal policies, which typically translates into unpredictable, and thus unreliable, behaviors. Following this intuition, we propose to impose a set of constraints into the DRL loop to guide the training process. These requirements, which typically encode domain expert knowledge, can be seen as suggestions that the agent should follow but is allowed to sometimes ignore if useful to maximize the reward signal. A foundational requirement for our work is finding a proper strategy to define and formally encode these constraints (which we refer to as \textit{rules}). In this thesis, we propose to exploit a formal language inherited from the software engineering community: scenario-based programming (SBP). For the actual training, we rely on the constrained reinforcement learning paradigm, proposing an extended version of the Lagrangian PPO algorithm. Recalling the parallelism with human beings, before being authorized to perform safety-critical operations, we must obtain a certification (e.g., a license to drive a car or a degree to perform medical operations). In the second part of this dissertation, we apply this concept in a deep reinforcement learning context, where the intelligent agents are controlled by artificial neural networks. In particular, we propose to perform a model selection phase after the training to find models that formally respect some given safety requirements before the deployment. However, DNNs have long been considered unpredictable black boxes and thus unsuitable for safety-critical contexts. Against this background, we build upon the emerging field of formal verification for neural networks to extend state-of-the-art approaches to robotic decision-making contexts. We propose ``ProVe", a verification tool for decision-making DNNs that quantifies the probability of violating the specified requirements. In the last chapter of this thesis, we provide a complete case study on a popular robotic problem: ``mapless navigation". Here, we show a concrete example of the application of our pipeline, starting from the definition of the requirements to the training and the final formal verification phase, to finally obtain a provably safe and effective agent

Online Safety Property Collection and Refinement for Safe Deep Reinforcement Learning in Mapless Navigation

Safety is essential for deploying Deep Reinforcement Learning (DRL) algorithms in real-world scenarios. Recently, verification approaches have been proposed to allow quantifying the number of violations of a DRL policy over input-output relationships, called properties. However, such properties are hard-coded and require task-level knowledge, making their application intractable in challenging safety-critical tasks. To this end, we introduce the Collection and Refinement of Online Properties (CROP) framework to design properties at training time. CROP employs a cost signal to identify unsafe interactions and use them to shape safety properties. Hence, we propose a refinement strategy to combine properties that model similar unsafe interactions. Our evaluation compares the benefits of computing the number of violations using standard hard-coded properties and the ones generated with CROP. We evaluate our approach in several robotic mapless navigation tasks and demonstrate that the violation metric computed with CROP allows higher returns and lower violations over previous Safe DRL approaches.Comment: Accepted at the 2023 IEEE International Conference on Robotics and Automation (ICRA). Marzari and Marchesini contributed equall