Simone Arreghini

Simone Arreghini, Cristina Iani, Alessandro Giusti et al.

This work compares three interaction modalities for human-robot collaboration: passive, reactive, and proactive. We studied 18 participants assembling a seven-layer colored tower from memory while using nearby and distant blocks. In the passive modality participants worked alone; in the reactive modality a mobile robot helped only upon request; in the proactive modality it initiated brick delivery and error signaling without explicit requests. Although robot assistance increased completion time, most participants preferred collaboration: 67% preferred proactive behavior and 78% judged it most useful. These results suggest that timely proactive support can improve user experience in controlled collaborative tasks.

Simone Arreghini, Alessandro Giusti, Alex Bordini et al.

Wheelchair users call for assistive mobility systems that provide active support, adapt to dynamic environments, and are intuitive and user-friendly. However, powered wheelchairs typically still provide limited autonomy and lack effective integration with advanced perception and navigation capabilities, particularly in complex real-world environments. This paper presents a preliminary study toward autonomous powered wheelchairs for real-world assistive mobility. We introduce a proof-of-concept prototype that integrates autonomous perception, gesture-based interaction, and navigation on a commercially available self-balancing powered wheelchair. The proposed system builds upon Genny Zero, a commercial self-balancing wheelchair that enables hands-free and intuitive operation through body-weight shifting. To extend its capabilities toward autonomous operation, we integrate an RGB-D camera for human-aware perception and interaction, together with a LiDAR sensor for localization and navigation. We demonstrate the integrated system in two assistive applications: (i) hailing, allowing users to call the wheelchair from a distance; and (ii) people-following, where the wheelchair follows a person using leader-follower strategies, including a constrained indoor navigation example. The results highlight the potential of combining autonomous robotics with assistive mobility platforms, while also showing the feasibility of the proposed integration and identifying the main technical challenges that must be addressed before moving toward user-ready, accessible, and intelligent mobility solutions. A video demonstrating the experimental setup and results is available at: https://youtu.be/LVAix_Qx7bM.

Simone Arreghini, Gabriele Abbate, Alessandro Giusti et al.

For a service robot, it is crucial to perceive as early as possible that an approaching person intends to interact: in this case, it can proactively enact friendly behaviors that lead to an improved user experience. We solve this perception task with a sequence-to-sequence classifier of a potential user intention to interact, which can be trained in a self-supervised way. Our main contribution is a study of the benefit of features representing the person's gaze in this context. Extensive experiments on a novel dataset show that the inclusion of gaze cues significantly improves the classifier performance (AUROC increases from 84.5% to 91.2%); the distance at which an accurate classification can be achieved improves from 2.4 m to 3.2 m. We also quantify the system's ability to adapt to new environments without external supervision. Qualitative experiments show practical applications with a waiter robot.