2 citations · 2 across the 10 of their papers we have counts for
13 papers
Anomaly Detection on Small Industrial Components via Vision-Based Tactile Sensing
G. F. Preziosa, M. Casiglia, M. Faroni +2
Automated inspection of small industrial components, including sub-centimetre-scale parts where defects are geometry-driven and poorly resolved by standard optical cameras, calls f…
Low-Resolution Perception for Robotic Packing
Giuseppe Fabio Preziosa, Federico Vignoni, Chiara Castellano +3
This work tackles the problem of scalable perception for robotic packing with low-cost, low-resolution depth sensing. We propose a framework where reconstruction cues drive next-vi…
Adaptive Human-Robot Collaborative Painting Combining Preference-Based Optimization and Dynamic Motion Primitives
C. Cella, M. Ristic, M. Faroni +2
This work presents a human-centered collaborative framework that integrates Preference-Based Optimization (PBO) and Dynamic Movement Primitives (DMPs) to optimize robot-assisted ta…
FRA-NBV: A Fast and Reflectivity-Aware Next-Best-View Strategy
G. F. Preziosa, E. Setti, M. Faroni +2
Autonomous 3D reconstruction with depth sensors is strongly affected by reflective surfaces, which cause missing or unreliable measurements and reduce the effectiveness of conventi…
Optimized Scheduling and Positioning of Mobile Manipulators in Collaborative Applications
Christian Cella, Sole Ester Sonnino, Marco Faroni +2
The growing integration of mobile robots in shared workspaces requires efficient path planning and coordination between the agents, accounting for safety and productivity. In this…
On Using Neural Networks to Learn Safety Speed Reduction in Human-Robot Collaboration: A Comparative Analysis
Marco Faroni, Alessio Spanò, Andrea M. Zanchettin +1
In Human-Robot Collaboration, safety mechanisms such as Speed and Separation Monitoring and Power and Force Limitation dynamically adjust the robot's speed based on human proximity…
Learning-Based Safety-Aware Task Scheduling for Efficient Human-Robot Collaboration
M. Faroni, A. Spano, A. M. Zanchettin +1
Ensuring human safety in collaborative robotics can compromise efficiency because traditional safety measures increase robot cycle time when human interaction is frequent. This pap…
Uncertainty-aware Planning with Inaccurate Models for Robotized Liquid Handling
Marco Faroni, Carlo Odesco, Andrea Zanchettin +1
Physics-based simulations and learning-based models are vital for complex robotics tasks like deformable object manipulation and liquid handling. However, these models often strugg…
Low Resolution Next Best View for Robot Packing
Giuseppe Fabio Preziosa, Chiara Castellano, Andrea Maria Zanchettin +2
Automating the packing of objects with robots is a key challenge in industrial automation, where efficient object perception plays a fundamental role. This paper focuses on scenari…
Automating Box Folding: Sequence Extraction and Ranking Methodologies
Giuseppe Fabio Preziosa, Davide Ferloni, Andrea Maria Zanchettin +2
Box folding represents a crucial challenge for automated packaging systems. This work bridges the gap between existing methods for folding sequence extraction and approaches focuse…
Digital Model-Driven Genetic Algorithm for Optimizing Layout and Task Allocation in Human-Robot Collaborative Assemblies
Christian Cella, Matteo Bruce Robin, Marco Faroni +2
This paper addresses the optimization of human-robot collaborative work-cells before their physical deployment. Most of the times, such environments are designed based on the exper…
Optimizing Collaborative Robotics since Pre-Deployment via Cyber-Physical Systems' Digital Twins
Christian Cella, Marco Faroni, Andrea Zanchettin +1
The collaboration between humans and robots re-quires a paradigm shift not only in robot perception, reasoning, and action, but also in the design of the robotic cell. This paper p…
dPMP-Deep Probabilistic Motion Planning: A use case in Strawberry Picking Robot
Alessandra Tafuro, Bappaditya Debnath, Andrea M. Zanchettin +1
This paper presents a novel probabilistic approach to deep robot learning from demonstrations (LfD). Deep movement primitives (DMPs) are deterministic LfD model that maps visual in…