Francesco Iodice , MSc
AI and Robotics Engineer
Human-Robot interaction, machine learning and computer vision
Center for Robotics and Intelligent Systems (CRIS),
Istituto Italiano di Tecnologia (IIT)
Via S. Quirico, 19d, 16163, Genova, Italy
Francesco earned his master’s degree in Artificial Intelligence and Robotics in 2019 from Sapienza University of Rome with a project named “Model Predictive Control of a mobile robot in a dynamic environment.” He has been pursuing a PhD in Bioengineering at Politecnico di Milano, Laboratory of Neuroengineering and Medical Robotics (NearLab), in collaboration with the Italian Institute of Technology (IIT), under the supervision of Dr. Arash Ajoudani at the Human-Robot Interfaces and Physical Interaction (HRI2) laboratory since 2020. His research focuses on computer vision, robotics, and machine learning with the objective of enhancing human ergonomics in highly dynamic human-robot-environment interactions. Since December 2022, he has also been a research fellow in artificial intelligence at Leonardo S.p.A.
Leader-follower roles and dynamic features
The objective is to extract leader-follower roles and dynamic features such as configuration-dependent stiffness (CDS) from videos of humans performing collaborative tasks (here, a two-person wood sawing), and replicating them in a dual-arm robotic setup. We created a dataset that is not dispersive in its classes but sectoral, i.e., dedicated exclusively to the industrial environment and human-robot collaboration. Specifically, we described our ongoing collection of the ’HRI30’ database for industrial action recognition from videos, containing 30 categories of industrial-like actions and 2940 manually annotated clips. The goal was to propose vision techniques for human action recognition and image classification, integrated into an augmented hierarchical quadratic programming (AHQP) scheme to hierarchically optimize the robot’s reactive behavior and human ergonomics. This framework allows the robot to be intuitively commanded in space while performing a task.