Aims: Testing the effect of the experimental robot-based therapeutic sessions for patients with dementia in: a controlled study of parallel groups of nursing home patients comparing the effects of therapy sessions utilizing a humanoid robot (NAO), an animal-shaped robot (PARO), or a trained dog (DOG), with conventional therapy (CONTROL) on symptoms of dementia; and an experience for patients who attend a day care center, comparing symptom prevalence and severity before and after sessions utilizing NAO and PARO. Methods: In the nursing home, patients were randomly assigned by blocks, based on dementia severity, to one of the 3 therapeutic groups to compare: CONTROL, PARO and NAO (phase 1) and CONTROL, PARO and DOG (phase 2). In the day care center, all patients received therapy with NAO (phase 1) and PARO (phase 2). Therapy sessions were held 2 days/week for 3 months. Evaluation at baseline and follow-up was carried out by blind raters using: the Global Deterioration Scale (GDS), the Severe Mini Mental State Examination (sMMSE), the Mini Mental State Examination (MMSE), the Neuropsychiatric Inventory (NPI), the Apathy Scale for Institutionalized Patients with Dementia Nursing Home version (APADEM-NH), the Apathy Inventory (AI) and the Quality of Life Scale (QUALID). Statistical analysis included descriptive statistics and non parametric tests performed by a blinded investigator. Results: In the nursing home, 101 patients (phase 1) and 110 patients (phase 2) were included. There were no significant differences at baseline. The relevant changes at follow-up were: (phase 1) patients in the robot groups showed an improvement in apathy; patients in NAO group showed a decline in cognition as measured by the MMSE scores, but not the sMMSE; the robot groups showed no significant changes between them; (phase 2) QUALID scores increased in the PARO group. In the day care center, 20 patients (phase 1) and 17 patients (phase 2) were included. The main findings were: (phase 1) imp

This paper presents an open-access platform for practical learning of intelligent robotics in engineering degrees: Robotics-Academy. It comprises a collection of exercises including recent service robot applications in real life, with different robots such as autonomous cars, drones or vacuum cleaners. It uses Robot Operating System (ROS) middleware, the de facto standard in robot programming, the 3D Gazebo simulator and the Python programming language. For each exercise, a software template has been developed, performing all the auxiliary tasks such as the graphical interface, connection to the sensors and actuators, timing of the code, etc. This also hosts the student’s code. Using this template, the student just focuses on the robot intelligence (for instance, perception and control algorithms) without wasting time on auxiliary details which have little educational value. The templates are coded as ROS nodes or as Jupyter Notebooks ready to use in the web browser. Reference solutions for illustrative purposes and automatic assessment tools for gamification have also been developed. An introductory course to intelligent robotics has been elaborated and its contents are available and ready to use at Robotics-Academy, including reactive behaviors, path planning, local/global navigation, and self-localization algorithms. Robotics-Academy provides a valuable complement to master classes in blended learning, massive online open courses (MOOCs) and online video courses, devoted to addressing theoretical content. This open educational tool connects that theory with practical robot applications and is suitable to be used in distance education. Robotics-Academy has been successfully used in several subjects on undergraduate and master’s degree engineering courses, in addition to a pre-university pilot course.

This paper presents a robotic platform, PiBot, which was developed to improve the teaching of robotics with vision to secondary students. Its computational core is the Raspberry Pi 3 controller board, and the greatest novelty of this prototype is the support developed for the powerful camera mounted on board, the PiCamera. An open software infrastructure written in Python language was implemented so that the student may use this camera as the main sensor of the robotic platform. Furthermore, higher-level commands were provided to enhance the learning outcome for beginners. In addition, a PiBot 3D printable model and the counterpart for the Gazebo simulator were also developed and fully supported. They are publicly available so that students and schools without the physical robot or that cannot afford to obtain one, can nevertheless practice, learn and teach Robotics using these open platforms: DIY-PiBot and/or simulated-PiBot.

This article presents a full course for autonomous aerial robotics inside the RoboticsAcademy framework. This “drone programming” course is open-access and ready-to-use for any teacher/student to teach/learn drone programming with it for free. The students may program diverse drones on their computers without a physical presence in this course. Unmanned aerial vehicles (UAV) applications are essentially practical, as their intelligence resides in the software part. Therefore, the proposed course emphasizes drone programming through practical learning. It comprises a collection of exercises resembling drone applications in real life, such as following a road, visual landing, and people search and rescue, including their corresponding background theory. The course has been successfully taught for five years to students from several university engineering degrees. Some exercises from the course have also been validated in three aerial robotics competitions, including an international one. RoboticsAcademy is also briefly presented in the paper. It is an open framework for distance robotics learning in engineering degrees. It has been designed as a practical complement to the typical online videos of massive open online courses (MOOCs). Its educational contents are built upon robot operating system (ROS) middleware (de facto standard in robot programming), the powerful 3D Gazebo simulator, and the widely used Python programming language. Additionally, RoboticsAcademy is a suitable tool for gamified learning and online robotics competitions, as it includes several competitive exercises and automatic assessment tools.