The whole globe has cranked up for coping with the COVID-19 situation. The hands-on tutorial targets at providing a comprehensive and pragmatic end-to-end walk-through for building an academic research paper recommender for the use case of COVID-19 related study, with the help of knowledge graph technology. The code examples that demonstrate the theories are reproducible and can hopefully provide value for researchers to build tools that support conducting research to find a cure to COVID-19.

There is an open discussion between those who defend mass-distributed models for humanoid robots and those in favor of simple concentrated models. Even though each of them has its advantages and disadvantages, little research has been conducted analyzing the control performance due to the mismatch between the model and the real robot, and how the simplifications affect the controller’s output. In this article we address this problem by combining a reduced model of the humanoid robot, which has an easier mathematical formulation and implementation, with a fractional order controller, which is robust to changes in the model parameters. This controller is a generalization of the well-known proportional–integral–derivative (PID) structure obtained from the application of Fractional Calculus for control, as will be discussed in this article. This control strategy guarantees the robustness of the system, minimizing the effects from the assumption that the robot has a simple mass distribution. The humanoid robot is modeled and identified as a triple inverted pendulum and, using a gain scheduling strategy, the performances of a classical PID controller and a fractional order PID controller are compared, tuning the controller parameters with a genetic algorithm.

The aim of this work is to model and simulate the humanoid robot HOAP-3 in the OpenHRP3 platform. Our purpose is to create a virtual model of the robot so that different motions and tasks can be tested in different environments. This will be the first step before testing the motion patterns in the real HOAP-3. We use the OpenHRP3 platform for the creation and validation of the robot model and tasks. The procedure followed to reach this goal is detailed in this article. In order to validate our experience, different walking motions are tested and the simulation results are compared with the experimental ones.

Facial gesture recognition is one of the main topics in HRI. We have developed a novel algorithm who allows to detect emotional states, like happiness, sadness or emotionless. A humanoid robot is able to detect these states with a ratio of success of 83% and interact in consequence. We use Active Appearance Models (AAMs) to determinate face features and classify the emotions using neural evolution, based on neural networks and differential evolution algorithm.