Federica Nenna

Ritratto Federica Nenna

Curriculum
Neuroscience, Technology, and Society, XXXV series
Grant sponsor

Dipartimento di Psicologia Generale, UNIPD
Supervisor

Luciano Gamberini
Co-supervisor

Mauro Conti
Contact
federica.nenna@phd.unipd.it

Project description
The constantly evolving area of Industry 4.0 is enabling greater use of virtual interfaces for teleoperating or simulating industrial robots (for a review, Wonsick and Padir, 2020). Virtual industrial robots have shown their utility for training novices (Pratticò et al., 2021; Roldan et al., 2019; Abidi et al., 2018), for programming work cycles offline (Damiani et al., 2018), or as interfaces allowing online teleoperations (Wang et al., 2019; Linn et al., 2017). For the present PhD project, a Universal Robot UR5 is faithfully reproduced in Virtual Reality (VR). A series of research experiments have thus been planned to assess human behavioral, cognitive and neural state when driving the robotic system in VR. The underlying topic of all experiments resides in the study of human motion as natural and intuitive control for virtual robotic systems. Particularly, in a first experiment (1), we ask which are the advantages of robotic virtualization by directly comparing performance and workload of users physically interacting with the virtual robotic arm and its physical counterpart. The second step of the project (2) aims at investigating the impact of different Human-Robot Interaction (HRI) modalities on user's behavior and cognition. Particularly, we ask whether driving the robotic arm by physically moving the own arm (motor control), rather than using a controller (buttons control), leads to better task performance and lower cognitive impact on the user. This research question extends to both young adults and senior users (3). Finally, we dive into the exploration of cognitive and neural aspects underlying mechanisms of motor control of the virtual robot. Specifically, we ask: (4) whether it is possible to enhance motor control in virtual robotic operations through multisensory integration, and (5) if and how a sense of embodiment, agency and ownership of the virtual robotic arm can be triggered and whether a higher embodiment is associated with better motor performance. All experiments involve self-reports, behavioral, motion capture and eye-tracking data. Moreover, mobile EEG is additionally used for investigating the last research question (5) through a MoBI (Mobile Brain/Body Imaging) approach. Overall, the present PhD project embraces different interdisciplinary fields and applies psychological, neuroscientific and computer science research to the manufacturing and engineering field.