Categories, Quantum Computing, and Swarm Robotics: A Case Study

The swarms of robots are examples of artificial collective intelligence, with simple individual autonomous behavior and emerging swarm effect to accomplish even complex tasks. Modeling approaches for robotic swarm development is one of the main challenges in this field of research. Here, we present a robot-instantiated theoretical framework and a quantitative worked-out example. Aiming to build up a general model, we first sketch a diagrammatic classification of swarms relating ideal swarms to existing implementations, inspired by category theory. Then, we propose a matrix representation to relate local and global behaviors in a swarm, with diagonal sub-matrices describing individual features and off-diagonal sub-matrices as pairwise interaction terms. Thus, we attempt to shape the structure of such an interaction term, using language and tools of quantum computing for a quantitative simulation of a toy model. We choose quantum computing because of its computational efficiency. This case study can shed light on potentialities of quantum computing in the realm of swarm robotics, leaving room for progressive enrichment and refinement

Robots as intelligent assistants to face COVID-19 pandemic

Motivation: The epidemic at the beginning of this year, due to a new virus in the coronavirus family, is causing many deaths and is bringing the world economy to its knees. Moreover, situations of this kind are historically cyclical. The symptoms and treatment of infected patients are, for better or worse even for new viruses, always the same: More or less severe flu symptoms, isolation and full hygiene. By now man has learned how to manage epidemic situations, but deaths and negative effects continue to occur. What about technology? What effect has the actual technological progress we have achieved? In this review, we wonder about the role of robotics in the fight against COVID. It presents the analysis of scientific articles, industrial initiatives and project calls for applications from March to now highlighting how much robotics was ready to face this situation, what is expected from robots and what remains to do. Results: The analysis was made by focusing on what research groups offer as a means of support for therapies and prevention actions. We then reported some remarks on what we think is the state of maturity of robotics in dealing with situations like COVID-19

Robot’s Inner Speech Effects on Human Trust and Anthropomorphism

Inner Speech is an essential but also elusive human psychological process that refers to an everyday covert internal conversation with oneself. We argued that programming a robot with an overt self-talk system that simulates human inner speech could enhance both human trust and users’ perception of robot’s anthropomorphism, animacy, likeability, intelligence and safety. For this reason, we planned a pre-test/post-test control group design. Participants were divided in two different groups, one experimental group and one control group. Participants in the experimental group interacted with the robot Pepper equipped with an over inner speech system whereas participants in the control group interacted with the robot that produces only outer speech. Before and after the interaction, both groups of participants were requested to complete some questionnaires about inner speech and trust. Results showed differences between participants’ pretest and post-test assessment responses, suggesting that the robot’s inner speech influences in participants of experimental group the perceptions of animacy and intelligence in robot. Implications for these results are discussed

A global workspace theory model for trust estimation in human-robot interaction

Successful and genuine social connections between humans are based on trust, even more when the people involved have to collaborate to reach a shared goal. With the advent of new findings and technologies in the field of robotics, it appears that this same key factor that regulates relationships between humans also applies with the same importance to human-robot interactions (HRI). Previous studies have proven the usefulness of a robot able to estimate the trustworthiness of its human collaborators and in this position paper we discuss a method to extend an existing state-of-the-art trust model with considerations based on social cues such as emotions. The proposed model follows the Global Workspace Theory (GWT) principles to build a novel system able to combine multiple specialised expert systems to determine whether the partner can be considered trustworthy or not. Positive results would demonstrate the usefulness of using constructive biases to enhance the teaming skills of social robots

An unusual Erdheim-Chester disease with orbital involvement: A case report

Erdheim-Chester disease is a rare non-Langerhans cell histiocytosis with multiorgan involvement and a specific tropism for perivascular and fatty connective tissue, of unclear origin, with poor response to therapy. Its identification is difficult because of the variable clinical presentation and its lack of knowledge. We report the case of a 63-years-old woman, with a history of bilateral orbital pseudotumor, who comes to our attention because of progressively worsening asthenia, vomiting and systemic inflammation. Total body computerized tomography scan showed a volumetric increase of choroid plexus of the temporal horn of the left lateral ventricle, presence of solid retrobulbar tissue at the level of both maxillary sinuses, lung fibrosis, and retroperitoneal and peri-aortic infiltration. The association of these signs addressed to a diagnosis of Erdheim-Chester disease. Thus, although extremely rare, the diagnosis of Erdheim-Chester disease must be considered in the case of bilateral retro-orbital tumors and multisystemic involvement

Inside the robot’s mind during human-robot interaction

Humans and robots collaborating and cooperating for pursuing a shared objective need to rely on the other for carrying out an effective decision process and for updating knowledge when necessary in a dynamic environment. Robots have to behave as they were human teammates. To model the cognitive process of robots during the interaction, we developed a cognitive architecture that we implemented employing the BDI (belief, desire, intention) agent paradigm. In this paper, we focus on how to let the robot show to the human its reasoning process and how its knowledge on the work environment grows. We realized a framework whose heart is a simulator that serves the human as a window on the robot’s mind