20 accepted papers
Image-guided biopsy is the most common technique for breast cancer diagnosis. Although magnetic resonance imaging (MRI) has the highest sensitivity in breast lesion detection, ultrasound (US) biopsy guidance is generally preferred due to its non-invasiveness and real-time image feedback during the i
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 p…
Latent variable models are widely used to perform unsupervised segmentation of time series in different context such as robotics, speech recognition, and economics. One of the most widely used latent variable model is the Auto-Regressive Hidden Markov Model (ARHMM), which combines a latent mode gove
Automation holds the potential to assist surgeons in robotic interventions, shifting their mental work load from visuomotor control to high level decision making. Reinforcement learning has shown promising results in learning complex visuomotor policies, especially in simulation environments where m
Advanced Sensing Technologies (ASTs) have a great potential to improve surgical quality and to further develop Surgical Robotic Systems (SRSs), enhancing their technical and autonomy capabilities. Among these sensing techniques, Electrical Bioimpedance (EBI) provides a non-invasive, low-cost, and sa
Flexible Endoscopes (FEs) for colonoscopy present several limitations due to their inherent complexity, resulting in patient discomfort and lack of intuitiveness for clinicians. Robotic FEs with autonomous control represent a viable solution to reduce the workload of endoscopists and the training ti…
Autonomous robotic surgery requires deliberation, i.e. the ability to plan and execute a task adapting to uncer-tain 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 i…
In this paper, we propose a novel bilateral teleoperation architecture that allows to optimally render the remote interaction force at the local side while guaranteeing a robustly stable behaviour. Stability is guaranteed by ensuring a proper energy exchange between the local and the remote sides. D…
The execution of surgical tasks by an Autonomous Robotic System (ARS) requires an up-to-date model of the current surgical environment, which has to be deduced from measurements collected during task execution. In this work, we propose to automate tissue dissection tasks by introducing a convolution
Intra-operative local 3D vessel representations have the potential to significantly decrease the use of contrast agents and exposure to ionizing radiation during endovascular procedures, while overcoming the 2D visualization limitation of fluoroscopic guidance. By fusing intravascular ultrasound (IV
When applied to industrial processes, predictive models (PMs) easily fail to depict the overall complexity of a production plant. Multiple factors can interfere during the production process and the requirements in accuracy, safety and efficiency are very high. To fully achieve the potential of PMs,
Deep Reinforcement Learning (DRL) is a viable solution for automating repetitive surgical subtasks due to its ability to learn complex behaviours in a dynamic environment. This task automation could lead to reduced surgeon’s cognitive workload, increased precision in critical aspects of the surgery,…
Robot-assisted surgery is an established clinical practice. The automatic identification of surgical actions is needed for a range of applications, including performance assessment of trainees and surgical process modeling for autonomous execution and monitoring. However, supervised action identific
The use of robots in minimally invasive surgery has improved the quality of standard surgical procedures. So far, only the automation of simple surgical actions has been investigated by researchers, while the execution of structured tasks requiring reasoning on the environment and the choice among m…
Automated surgical gestures classification and recognition are important precursors for achieving the goal of objective evaluation of surgical skills. Many works have been done to discover and validate metrics based on the motion of instruments that can be used as features for automatic classificati…
Reinforcement Learning (RL) methods have demonstrated promising results for the automation of subtasks in surgical robotic systems. Since many trial and error attempts are required to learn the optimal control policy, RL agent training can be performed in simulation and the learned behavior can be t…
Robotics has the potential to become a revolutionary technology in the next future and if we want to spread it among the population we must start accounting for affordability in robot research and design. Plastic robots are an emerging example of affordable design where expensive metal structures ar
A torque-controlled back-support exoskeleton to assist manual handling is presented. Its objective is to provide a significant portion of the forces necessary to carry out the physical task, thereby reducing the compressive loads on the lumbar spine and the associated risk of injury. The design rati
Robust representation of actions and its sequences for complex robotic tasks would transform robot's understanding to execute robotic tasks efficiently. The challenge is to understand action sequences for highly unstructured environments and to represent and construct action and action sequences. In…
Deformations are an essential aspect of our interaction with real bodies, prompting the development of many modelling and simulation methods for virtual environments. Some of the methods address specific classes of interaction, e.g. pushing, grabbing, cutting or needle insertion, whereas hybrid appr