Publications

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Despite the importance of human-robot collaborative transportation of flexible material in many industrial scenarios, many works in the literature assume a passive role for the robot during the collaboration. The robot can only follow the human partner, without providing assistance in the more challenging phase of the collaboration such as precise material positioning. This work presents a framework for co-transportation, proposing a distance-based policy for dynamic leader role allocation through the task. For large distances from the target pose, the robot is mainly controlled by vision-based manual guidance exploiting haptic feedback and 3D human pose information; instead, close to the target material position, the robot acts as a leader guiding the human operator. The proposed framework is evaluated considering a carbon fiber draping task, which requires both co-transportation and precise positioning of flexible materials. Experimental results demonstrate how the robot leading the task in the final stage allows to achieve high task efficiency and alleviates human stress in the execution of the task.

This work proposes an innovative people-aware navigation for telepresence robots in a populated environment based on the estimated inclination of people to interact and the context information. The main novelty of the proposed people-aware shared intelligence is the ability to fuse the remote operator’s commands with the probability of person-robot interaction—from both the operator driving the robot and the people around it—and translate it into semi-autonomous approaching and avoiding behaviors that are not coded a priori but rather dynamically emerge according to the current context-awareness. Experiments involved 45 healthy participants who evaluated the proposed approach on a real robot. Three conditions have been tested: (a) the new people-aware shared intelligence; (b) a shared intelligence system integrated with the standard ROS social navigation layers and; (c) a direct teleoperation (i.e., no robot’s intelligence). Results from our people-aware shared intelligence system have shown that the robot’s social behaviors were in line with the expectations of the participants in terms of comfort, naturalness, and sociability and coherent with the findings from previous studies. Furthermore, the proposed system has facilitated the social interaction between the remote operator and the surrounding people, making the robot more proactive and without affecting navigation performance.

Educational robotics is increasingly spreading in schools, also with the aim of fostering young women’s interest in STEM disciplines, particularly in programming and Artificial Intelligence. However, it is crucial to design and select robots that resonate emotionally with female students to overcome gender stereotypes that traditionally deter them from computer science disciplines. This study explores the hypothesis that educational robots should be specifically tailored to meet the expectations and interests of female students. An experiment was conducted with 211 participants, equally divided by gender, who were asked to evaluate images of 16 different educational robots using a semantic differential scale. The results reveal differences between males and females in the attitudes and opinions towards educational robots. While both genders generally rated the robots as more masculine than feminine, female participants tended to provide higher overall scores, except for specific robots. Additionally, robots that were perceived as more feminine were often rated as simpler whereas masculine robots are associated to the words intelligent and creative, reflecting established societal stereotypes. These insights suggest that educational robots should be designed to appeal to both girls and boys, avoiding reinforcing gender stereotypes and ensuring inclusivity in STEM education. Further research is necessary to explore these attitudes and their implications for fostering a more balanced interest in STEM among both genders.