PeppeRecycle: Improving Children’s Attitude Toward Recycling by Playing with a Social Robot

In this paper, we investigate the use of a social robot as an engaging interface of a serious game intended to make children more aware and well disposed towards waste recycle. The game has been designed as a competition between the robot Pepper and a child. During the game, the robot simultaneously challenges and teaches the child how to recycle waste materials. To endow the robot with the capability to play as a game opponent in a real-world context, it is equipped with an image recognition module based on a Convolutional Neural Network to detect and classify the waste material as a child would do, i.e. by simply looking at it. A formal experiment involving 51 primary school students is carried out to evaluate the effectiveness of the game in terms of different factors such as the interaction with the robot, the users’ cognitive and affective dimensions towards ecological sustainability, and the propensity to recycle. The obtained results are encouraging and draw promising scenarios for educational robotics in changing children’s attitudes toward recycling. Indeed Pepper turns out to be positively evaluated by children as a trustful and believable companion and this allows children to be concentrated on the “memorization” task during the game. Moreover, the use of real objects as waste items during the game turns out to be a successful approach not only for perceived learning effectiveness but also for the children’s engagement

A novel integrated industrial approach with cobots in the age of industry 4.0 through conversational interaction and computer vision

From robots that replace workers to robots that serve as helpful colleagues, the field of robotic automation is experiencing a new trend that represents a huge challenge for component manufacturers. The contribution starts from an innovative vision that sees an ever closer collaboration between Cobot, able to do a specific physical job with precision, the AI world, able to analyze information and support the decision-making process, and the man able to have a strategic vision of the future

Design, synthesis and biological evaluation of novel bicyclo[1.1.1]pentane-basedx-acidic amino acids as glutamate receptors ligands

A novel series of bicyclo[1.1.1]pentane-based x-acidic amino acids, including (2S)- and (2R)-3-(30-carboxybicyclo[ 1.1.1]pentyl)alanines (8 and 9), (2S)- and (2R)-2-(30-carboxymethylbicyclo[1.1.1]pentyl)glycines (10 and 11), and (2S)- and (2R)-3-(30-phosphonomethylbicyclo[1.1.1]pentyl)glycines (12 and 13), were synthesized and evaluated as glutamate receptor ligands. Among them, (2R)-3-(30-phosphonomethylbicyclo[ 1.1.1]pentyl)glycine (13) showed relatively high affinity and selectivity at the NMDA receptor. The results are also discussed in light of pharmacophoric modelling studies of NMDA agonists and antagonists

Automatic facial emotion recognition at the COVID-19 pandemic time

People use various nonverbal communicative channels to convey emotions, among which facial expressions are considered the most important ones. Thus, automatic Facial Expression Recognition (FER) is a fundamental task to increase the perceptive skills of computers, especially in human-computer interaction. Like humans, state-of-art FER systems are able to recognize emotions from the entire face of a person. However, the COVID-19 pandemic has imposed a massive use of face masks that help in preventing infection but may hamper social communication and make the recognition of facial expressions a very challenging task due to facial occlusion. In this paper we propose a FER system capable to recognize emotions from masked faces. The system checks for the presence of a mask on the face image and, in case of mask detection, it extracts the eyes region and recognizes the emotion only considering that portion of the face. The effectiveness of the developed FER system was tested in recognizing emotions and their valence only from the eyes region and comparing the results when considering the entire face. As it was expected, emotions that are related mainly to the mouth region (e.g., disgust) are barely recognized, while positive emotions are better identified by considering only the eyes region. Moreover, we compared the results of our FER system to the human annotation of emotions on masked faces. We found out that the FER system outperforms the human annotation, thus showing that the model is able to learn proper features for each emotion leveraging only the eyes region

"Keep the user in mind!’ Persuasive Effects of Social Robot as Personalized Nutritional coach

In this paper we investigate the use of a social robot as an interface for providing personalized information about nutrition. In particular, we evaluated the effect of message adaptation to some demographic traits that are automatically recognized by a social robot, Pepper in this case. The proposed approach is based on soft biometrics and it can estimate several traits simultaneously, such as gender and age of people in the field of view of the robot. Our hypothesis is that adding this capability to a social robot improves the persuasive effects, in terms of perceived informational quality, motivational strength and social believability, and the recall of the message. We performed a preliminary experimental study and its results seem to support the persuasiveness of the personalized nutritional coac

Social Assistive Robots in Elderly Care: Exploring the role of Empathy

The COVID-19 emergency has shown that elderly people living in Assisted Living Houses (ALHs) have been highly exposed to the virus. Besides health problems, during the social distancing restrictions, the elderly were also strongly affected by loneliness due to a lack of contact with their loved ones. Innovative solutions for ALH based on Social Assistive Robotics can reduce the risk of infection and, at the same time, improve the quality of life of elderly people. In this work, after a brief overview on the Pepper4Elderly project, we focus on the role of empathy and affective behaviors in human-robot interaction when the robot is used as a caring agent to assist and entertain the elderly guests of ALHs

Learning waste recycling by playing with a social robot

In this paper we investigate the use of a social robot as an interface to a serious game aiming to train kids in how to recycle materials correctly. Serious games are mostly used to induce motivations and engagement in users and support knowledge transfer during playing. They are especially effective when the goal of the game concerns behavior change. In addition, social robots have been used effectively in educational settings to engage children in the learning process. Following this trend, we designed a serious game in which the social robot Pepper plays with a child to teach him to correctly recycle the materials. To endow the robot with the capability of detecting and classifying the waste material we developed an image recognition module based on a Convolutional Neural Network. Preliminary experimental results show that the implementation of a serious game about recycling into the Pepper robot improves its social behavior. The use of real objects as waste items during the game turns out to be a successful approach not only for perceived learning effectiveness but also for engagement of the children

Signal Transducer and Activator of Transcription 1 Plays a Pivotal Role in RET/PTC3 Oncogene-induced Expression of Indoleamine 2,3-Dioxygenase 1

Indoleamine 2,3-dioxygenase 1 (IDO1) is a single chain oxidoreductase that catalyzes tryptophan degradation to kynurenine. In cancer, it exerts an immunosuppressive function as part of an acquired mechanism of immune escape. Recently, we demonstrated that IDO1 expression is significantly higher in all thyroid cancer histotypes compared with normal thyroid and that its expression levels correlate with T regulatory (Treg) lymphocyte densities in the tumor microenvironment. BRAF(V600E)- and RET/PTC3-expressing PcCL3 cells were used as cellular models for the evaluation of IDO1 expression in thyroid carcinoma cells and for the study of involved signal transduction pathways. BRAF(V600E)-expressing PcCL3 cells did not show IDO1 expression. Conversely, RET/PTC3-expressing cells were characterized by a high IDO1 expression. Moreover, we found that, the STAT1-IRF1 pathway was instrumental for IDO1 expression in RET/PTC3 expressing cells. In detail, RET/PTC3 induced STAT1 overexpression and phosphorylation at Ser-727 and Tyr-701. STAT1 transcriptional regulation appeared to require activation of the canonical NF-κB pathway. Conversely, activation of the MAPK and PI3K-AKT pathways primarily regulated Ser-727 phosphorylation, whereas a physical interaction between RET/PTC3 and STAT1, followed by a direct tyrosine phosphorylation event, was necessary for STAT1 Tyr-701 phosphorylation. These data provide the first evidence of a direct link between IDO1 expression and the oncogenic activation of RET in thyroid carcinoma and describe the involved signal transduction pathways. Moreover, they suggest possible novel molecular targets for the abrogation of tumor microenvironment immunosuppression. The detection of those targets is becoming increasingly important to yield the full function of novel immune checkpoint inhibitors