Human-robot cooperation for teleoperation in robotic surgery

Abstract

This thesis investigates data-driven methods for implementing human-robot interaction in the da Vinci Research Kit (dVRK) platform, with a particular focus on skill modeling, integration of autonomy in the surgical workflow, and real-time performance assessment. As robotic surgery becomes increasingly prevalent, there is a growing need for intelligent systems that can both assist human operators and also understand and adapt to their actions in real-time. This work addresses this challenge by developing a high performing control framework through a sequence of studies that span through the different types of controllers in the spectrum of teleoperative human-robot cooperation and skill assessment: shared control, supervisory control, and assistive control. Early chapters focus on shared control and supervisory control frameworks, introducing novel methods for learning bimanual surgical trajectories from demonstrations, enabling robots to generalise motion patterns from expert behavior. The subsequent work explores control paradigms that modulate robotic assistance in a data-driven approach, balancing autonomy and human input to achieve both higher performances and lower perceived workload. Objective evaluations demonstrate that adaptive assistance can benefit novice users without significantly impeding expert performance. Building upon these foundations, the last chapter shifts focus to real-time surgical skill assessment using deep learning models trained on multimodal inputs. These systems are designed to provide frame-level predictions of technical skill, enabling continuous feedback during task execution. Across all studies, this thesis emphasises the integration of expert data, task structure, and real-time capabilities to build responsive surgical systems that can fit into the surgical workflow. The contributions are validated with user studies and supported by extensive experimentation on datasets. User studies with participants show improved performance and perceived workload throughout all experiments when using the proposed control systems. Together, these contributions advance the state-of-the-art in human-robot cooperation and provide a foundation for intelligent systems in surgical assistance.Open Acces