In human-robot collaboration, current widely used human-robot communication is mainly based on audio and haptic mediums. However, this kind of communication is stiff and mechanical. Inspired by human-human communication in which vision and hearing contribute over 88% for human perception, we propose a knowledge-driven audio-visual virtual agent system, which allows collaborative robots to present its knowledge and feelings in a human-like way. During the collaboration training process, the virtual agent will build its assembly knowledge of how to work with the co-worker based on inverse reinforcement learning. To deploy a co-assembly task with its human partner, the virtual agent will also be able to produce assembly knowledge-based responses, which include knowledge-driven speech and speech synchronized facial animations. By leveraging the proposed knowledge-driven virtual agent, the collaborative robot not only can fulfill the co-assembly task but also can communicate with human partner in a more natural way.

This paper addresses commonsense knowledge (CSK) to enhance human-robot collaboration (HRC) in large scale smart manufacturing. As big data in collaborative robotics grows, CSK in useful to achieve task optimization as depicted in our simulation studies and laboratory experiments, extendable to real-world applications.

The trust of a robot in its human partner is a significant issue in human-robot interaction, which is seldom explored in the field of robotics. This study addresses a critical issue of robots' trust in humans during the human-robot collaboration process based on the data of human motions, past interactions of the human-robot pair, and the human's current performance in the co-carry task. The trust level is evaluated dynamically throughout the collaborative task that allows the trust level to change if the human performs false positive actions, which can help the robot avoid making unpredictable movements and causing injury to the human. Experimental results showed that the robot effectively assisted the human in collaborative tasks through the proposed computational trust model.

The novel coronavirus (Covid-19) has spread rapidly amongst countries all around the globe. Compared to the rise in cases, there are few Covid-19 testing kits available. Due to the lack of testing kits for the public, it is useful to implement an automated AI-based E-health decision support system as a potential alternative method for Covid-19 detection. As per medical examinations, the symptoms of Covid-19 could be somewhat analogous to those of pneumonia, though certainly not identical. Considering the enormous number of cases of Covid-19 and pneumonia, and the complexity of the related images stored, the data pertaining to this problem of automated detection constitutes big data. With rapid advancements in medical imaging, the development of intelligent predictive and diagnostic tools have also increased at a rapid rate. Data mining and machine learning techniques are widely accepted to aid medical diagnosis. In this paper, a huge data set of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal healthy cases are utilized for AI-based decision support in detecting the Coronavirus disease. The transfer learning approach, which enables us to learn from a smaller set of samples in a problem and transfer the discovered knowledge to a larger data set, is employed in this study. We consider transfer learning using three different models that are pre-trained on several images from the ImageNet source. The models deployed here are VGG16, VGG19, and ResNet101. The dataset is generated by gathering different classes of images. We present our approach and preliminary evaluation results in this paper. We also discuss applications and open issues.