Publications

9

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.

This work introduces a real-time intention decoding algorithm grounded in muscle synergies (Syn-ID). The algorithm detects the electromyographic (EMG) onset and infers the direction of the movement during reaching tasks to control a powered shoulder-elbow exoskeleton. Features related to muscle synergies are used in a Gaussian Mixture Model and probability accumulation-based logic to infer the user’s movement direction. The performance of the algorithm was verified by a feasibility study including eight healthy participants. The experiments comprised a transparent session, during which the exoskeleton did not provide any assistance, and an assistive session in which the Syn-ID strategy was employed. Participants were asked to reach eight targets equally spaced on a circumference of 25 cm radius (adjusted chance level: 18.1%). The results showed an average accuracy of 48.7% after 0.6 s from the EMG onset. Most of the confusion of the estimate was found along directions adjacent to the actual one (type 1 error: 33.4%). Effects of the assistance were observed in a statistically significant reduction in the activation of Posterior Deltoid and Triceps Brachii. The final positions of the movements during the assistive session were on average 1.42 cm far from the expected ones, both when the directions were estimated correctly and when type 1 errors occurred. Therefore, combining accurate estimates with type 1 errors, we computed a modified accuracy of 82.10±6.34%. Results were benchmarked with respect to a purely kinematics-based approach. The Syn-ID showed better performance in the first portion of the movement (0.14 s after EMG onset).

Robotic waste sorting poses significant challenges in both perception and manipulation, given the extreme variability of objects that should be recognized on a cluttered conveyor belt. While deep learning has proven effective in solving complex tasks, the necessity for extensive data collection and labeling limits its applicability in real-world scenarios like waste sorting. To tackle this issue, we introduce a data augmentation method based on a novel GAN architecture called wasteGAN. The proposed method allows to increase the performance of semantic segmentation models, starting from a very limited bunch of labeled examples, such as few as 100. The key innovations of wasteGAN include a novel loss function, a novel activation function, and a larger generator block. Overall, such innovations helps the network to learn from limited number of examples and synthesize data that better mirrors real-world distributions. We then leverage the higher-quality segmentation masks predicted from models trained on the wasteGAN synthetic data to compute semantic-aware grasp poses, enabling a robotic arm to effectively recognizing contaminants and separating waste in a real-world scenario. Through comprehensive evaluation encompassing dataset-based assessments and real-world experiments, our methodology demonstrated promising potential for robotic waste sorting, yielding performance gains of up to 5.8% in picking contaminants. The project page is available at https://github.com/bach05/wasteGAN.git.

Panoramic cameras offer a 4π steradian field of view, which is desirable for tasks like people detection and tracking since nobody can exit the field of view. Despite the recent diffusion of low-cost panoramic cameras, their usage in robotics remains constrained by the limited availability of datasets featuring annotations in the robot space, including people’s 2D or 3D positions. To tackle this issue, we introduce PanNote, an automatic annotation tool for people’s positions in panoramic videos. Our tool is designed to be cost-effective and straightforward to use without requiring human intervention during the labeling process and enabling the training of machine learning models with low effort. The proposed method introduces a calibration model and a data association algorithm to fuse data from panoramic images and 2D LiDAR readings. We validate the capabilities of PanNote by collecting a real-world dataset. On these data, we compared manual labels, automatic labels and the predictions of a baseline deep neural network. Results clearly show the advantage of using our method, with a 15-fold speed up in labeling time and a considerable gain in performance while training deep neural models on automatically labelled data.

Brain-computer interfaces (BCIs) represent an alternative channel of communication between the user and the external environment, circumventing the need for traditional neural pathways. The capability to modulate one’s own electroencephalogram (electroencephalography (EEG)) signal holds the potential to facilitate specific movements in external devices, thereby restoring or enhancing certain abilities that may have been compromised. In this study, we propose two potential configurations for a four-class, closed loop, real-time Motor Imagery (MI) brain-computer interface (BCI), with the objective of assessing the viability of these endogenous BCIs in accurately directing a cursor on the screen. Twelve healthy participants and one individual with motor disability participated in the experiment, with nine of them successfully transitioning from one-dimensional to two-dimensional cursor control. This outcome suggests that proficient control is achievable with sufficient training time.