Federico Cunico

Purpose: Automation in robot-assisted surgery (RAS) requires not only accurate scene understanding but also real-time reasoning and action within dynamic surgical workflows. This work introduces SPARTAN: the S urgical P eg- A nd- R ing T riplet and Workflow AN ticipation Benchmark, alongside a unified baseline for real-time surgical workflow analysis, for the first time jointly addressing surgical phase recognition, phase anticipation, and action triplet recognition. This integrated design bridges high-level workflow understanding with fine-grained, robot-action-level perception. Methods: The SPARTAN benchmark is based on a modified Peg-and-Ring training task performed on the da Vinci Research Kit (dVRK), providing frame-level annotations of surgical phases and dual-arm action triplets that delineate initial, intermediate, and final workflow states. Results: We demonstrate that our baseline achieves performance comparable to state-of-the-art methods across all three SPARTAN tasks while operating in real time. The benchmark offers complexity comparable to related datasets in terms of phase structure, number of videos, and triplet diversity, yet remains reproducible and directly applicable to physical robotic systems. Conclusion: SPARTAN provides a practical foundation for developing and evaluating real-time perception and reasoning models in RAS.

To enable flexible, high-throughput automation in settings where people and robots share workspaces, collaborative robotic cells must reconcile stringent safety guarantees with the need for responsive and effective behavior. A dynamic obstacle is the stochastic, task-dependent variability of human motion: when robots fall back on purely reactive or worst-case envelopes, they brake unnecessarily, stall task progress, and tamper with the fluidity that true Human-Robot Interaction demands. In recent years, learning-based human-motion prediction has rapidly advanced, although most approaches produce worst-case scenario forecasts that often do not treat prediction uncertainty in a well-structured way, resulting in over-conservative planning algorithms, limiting their flexibility. We introduce Uncertainty-Aware Predictive Control Barrier Functions (UA-PCBFs), a unified framework that fuses probabilistic human hand motion forecasting with the formal safety guarantees of Control Barrier Functions. In contrast to other variants, our framework allows for dynamic adjustment of the safety margin thanks to the human motion uncertainty estimation provided by a forecasting module. Thanks to uncertainty estimation, UA-PCBFs empower collaborative robots with a deeper understanding of future human states, facilitating more fluid and intelligent interactions through informed motion planning. We validate UA-PCBFs through comprehensive real-world experiments with an increasing level of realism, including automated setups (to perform exactly repeatable motions) with a robotic hand and direct human-robot interactions (to validate promptness, usability, and human confidence). Relative to state-of-the-art HRI architectures, UA-PCBFs show better performance in task-critical metrics, significantly reducing the number of violations of the robot's safe space during interaction with respect to the state-of-the-art.

Ge'ez, an ancient Ethiopic script of cultural and historical significance, has been largely neglected in handwriting recognition research, hindering the digitization of valuable manuscripts. Our study addresses this gap by developing a state-of-the-art Ge'ez handwriting recognition system using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Our approach uses a two-stage recognition process. First, a CNN is trained to recognize individual characters, which then acts as a feature extractor for an LSTM-based system for word recognition. Our dual-stage recognition approach achieves new top scores in Ge'ez handwriting recognition, outperforming eight state-of-the-art methods, which are SVTR, ASTER, and others as well as human performance, as measured in the HHD-Ethiopic dataset work. This research significantly advances the preservation and accessibility of Ge'ez cultural heritage, with implications for historical document digitization, educational tools, and cultural preservation. The code will be released upon acceptance.

Continuous monitoring of coma patients is essential but challenging, especially in developing countries with limited resources, staff, and infrastructure. This paper presents a low-cost IoT-based system designed for such environments. It uses affordable hardware and robust software to monitor patients without constant internet access or extensive medical personnel. The system employs cost-effective sensors to track vital signs, including heart rate, body temperature, blood pressure, eye movement, and body position. An energy-efficient microcontroller processes data locally, synchronizing with a central server when network access is available. A locally hosted app provides on-site access to patient data, while a GSM module sends immediate alerts for critical events, even in areas with limited cellular coverage. This solution emphasizes ease of deployment, minimal maintenance, and resilience to power and network disruptions. Using open-source software and widely available hardware, it offers a scalable, adaptable system for resource-limited settings. At under $30, the system is a sustainable, cost-effective solution for continuous patient monitoring, bridging the gap until more advanced healthcare infrastructure is available.

The revolutionary technologies behind Industry 4.0 have opened a new era for manufacturing: connected and autonomous machines, collaborative robotics, and monitoring techniques are spreading to increase productivity and sustainability. From the workers’ perspective, they bring new safety threats but also opportunities to solve old ones, while concerns about workers’ privacy arise due to the increase of data sensed and transferred from the shop floor. This paper presents the results of a research project addressing the prediction of dangerous conditions through workplace monitoring with privacy guarantees. This work is driven by a realistic approach starting from the fact that it is entirely centered on a real 14-meter production line equipped with an extensive array of top-tier devices, including robotic arms, autonomous mobile robots, a reconfigurable moving belt, a multi-camera system, and a highly efficient data transport and computation infrastructure. This project shows safety and privacy achievements over six representative use cases such as man-on-the-ground, environmental events (e.g., fire incidents), workers’ errors that can lead to potential accidents, compliance of Personal Protective Equipment (PPE), and gatherings restrictions. The benefits of this study extend to stakeholders such as manufacturers and workers offering safety systems that can be deployed in industrial settings while addressing privacy concerns and providing compliance with regulations. The industrial laboratory at the heart of this study represents with realism a dynamic and interconnected Industry 4.0 and 5.0 environment.