Human-robot collaboration has great potential in enhancing robot deployment at close proximity with people, especially in non-dyadic collaborations with multiple users. However, autonomous systems that are capable of handling such interactions in a physical domain are rare. This work proposes TriHRCBot, a robotic architecture designed to handle a collaborative task that involves two concurrent users. The architecture is sensitive to position, orientation, body lengths and state of the users in the interaction, and uses this information to adjust the pose of a target object to enable both users to act on it at the same time. A robotic system equipped with the TriHRCBot architecture was deployed in a user study in which 30 participants from the BAE Systems Academy for Skills and Knowledge Centre interacted with it during such multi-user collaborative task. The study shows that the participants considered TriHRCBot acceptable for the task at hand. The code repository of the architecture is publicly available11Code repository: https://github.com/francescosemeraro/TriHRCBot,

The advancement of collaborative robots emphasizes fast, efficient, and adaptive learning, particularly in environments with visual occlusions. While deep learning has proven effective in robotics, its reliance on large datasets, high computational resources, and limited interpretability poses significant challenges. To address these issues, we explored the use of the Compact Prediction Tree as an efficient and explainable machine learning approach for sequential pattern recognition. We used this algorithm in a human-robot collaboration scenario, where a robot assisted a user to assemble a cubic scaffold by handing over the correct assembly pieces, even under partial visibility of the workspace. Experimental validations showed that the system completed the task perfectly when a single component of the assembly was occluded, and maintained an average task completion rate of at least 0.8 when two components were occluded, even when trained on only 20% of the dataset.The code repository of the robotic system is publicly available1.

Human-robot collaboration has the potential of unlocking new manufacturing paradigms thanks to the introduction of a robotic architecture in a production chain that involves human workers. A possible innovative declination of this is the use of collaborative robots to enable two workers to concurrently act on the same manufacturing target without causing mutual disturbances. By doing so, the efficiency of the process would be preserved while reducing the production times. This work designs a physical collaborative task that involves two users and one collaborative robot. The users act in the scenario in a concurrent way on the same target object, while the robot physically intervenes in the scene as a mediator by adjusting the position and orientation of the object to accommodate both users at the same time. Through this experimental setup, 78 apprentices and teachers of the BAE Systems Academy for Skills and Knowledge Centre were recruited to investigate the users’ perception of the task workload and trust towards the robotic system. Specifically, they performed the same task under two experimental conditions, in which the robot responded to changes in the interaction in a reactive or timed way, respectively. The statistical analysis showed that a timed response of the robot was associated with lower perceived workload and higher predictability of the system.

Human-robot interaction (HRI) research is progressively addressing multi-party scenarios, where a robot interacts with more than one human user at the same time. Conversely, research is still at an early stage for human-robot collaboration The use of machine learning techniques to handle such type of collaboration requires data that are less feasible to produce than in a typical HRC setup. This work outlines scenarios of concurrent tasks for non-dyadic HRC applications. Based upon these concepts, this study also proposes an alternative way of gathering data regarding multi-user activity, by collecting data related to single users and merging them in post-processing, to reduce the effort involved in producing recordings of pair settings. To validate this statement, 3D skeleton poses of activity of single users were collected and merged in pairs. After this, such datapoints were used to separately train a long short-term memory (LSTM) network and a variational autoencoder (VAE) composed of spatio-temporal graph convolutional networks (STGCN) to recognise the joint activities of the pairs of people. The results showed that it is possible to make use of data collected in this way for pair HRC settings and get similar performances compared to using training data regarding groups of users recorded under the same settings, relieving from the technical difficulties involved in producing these data. The related code and collected data are publicly available11The code repository is available at: https://github.com/francescosemeraro/Multi_User_through_Single_User.git. The data repository is available at: https://figshare.com/s/64bd023e968e6eb096f3..

Internet of Robotic Things paradigm offers a concrete support to daily life. The pervasiveness of smart things, together with advances in cloud robotics, can help the smart systems to perceive and collect more information about the users and the environment. Often citizens have experienced “one-size-fits-all” approach, since the delivered service was not personalized, therefore resulting far from user's expectations. Hence, future smart agents, like robots, should produce personalized behaviours based on user emotions and moods in order to be more integrated into ordinary activities. In this work, we investigated the performances with unsupervised and supervised approaches to recognize three different moods elicited during a social interaction by means of a wearable system capable of measuring the Electrocardiogram, the ElectroDermal Activity and the Electroencephalographic signals. Particularly, the classification problem was analysed using three unsupervised (K-Mean, Self-Organizing Map and Hierarchical Clustering) and three supervised methods (Support Vector Machine, Decision Tree and k-nearest neighbour). The supervised algorithms reached an accuracy of 0.86 in the best case. The outcomes show that even in an unsupervised context the system is able to recognize the mood, reaching an accuracy equal to 0.76 in the best case.