To benefit from the social capabilities of a robot math tutor, instead of being distracted by them, a novel approach is needed where the math task and the robot's social behaviors are better intertwined. We present concrete design specifications of how children can practice math via a personal conversation with a social robot and how the robot can scaffold instructions. We evaluated the designs with a three-session experimental user study (n = 130, 8-11 y.o.). Participants got better at math over time when the robot scaffolded instructions. Furthermore, the robot felt more as a friend when it personalized the conversation.
MULTIFILE
Hospitalisation is stressful for children. Play material is often offered for distraction and comfort. Weexplored how contact with social robot PLEO could positively affect a child’s well-being. To this end, we performed a multiple case study on the paediatric ward of two hospitals. Child life specialists offered PLEO as a therapeutic activity to children in a personalised way for a well-being related purpose in three to five play like activity sessions during hospital visits/stay. Robot–child interaction was observed; care professionals, children and parents were interviewed. Applying direct content analysis revealed six categories of interest: interaction with PLEO, role of the adults, preferences for PLEO, PLEO as buddy, attainment of predetermined goal(s) and deployment of PLEO. Four girls and five boys, aged 4–13, had PLEO offered as a relief from stress or boredom or for physical stimulation. All but one started interacting with PLEO and showed behaviours like hugging, caring or technical exploration, promoting relaxation, activation and/or making contact. Interaction with PLEO contributed to achieving the well-being related purpose for six of them. PLEO was perceived as attractive to elicit play. Although data are limited, promising results emerge that the well-being of hospitalised children might be fostered by a personalised PLEO offer.
DOCUMENT
To benefit from the social capabilities of a robot math tutor, instead of being distracted by them, a novel approach is needed where the math task and the robot's social behaviors are better intertwined. We present concrete design specifications of how children can practice math via a personal conversation with a social robot and how the robot can scaffold instructions. We evaluated the designs with a three-session experimental user study (n = 130, 8-11 y.o.). Participants got better at math over time when the robot scaffolded instructions. Furthermore, the robot felt more as a friend when it personalized the conversation.
MULTIFILE
Deploying robots from indoor to outdoor environments (vise versa) with stable and accurate localization is very important for companies to secure the utilization in industrial applications such as delivering harvested fruits from plantations, deploying/docking, navigating under solar panels, passing through tunnels/underpasses and parking in garages. This is because of the sudden changes in operational conditions such as receiving high/low-quality satellite signals, changing field of view, dealing with lighting conditions and addressing different velocities. We observed these limitations especially in indoor-outdoor transitions after conducting different projects with companies and obtaining inaccurate localization using individual Robotics Operating Systems (ROS2) modules. As there are rare commercial solutions for IO-transitions, AlFusIOn is a ROS2-based framework aims to fuse different sensing and data-interpretation techniques (LiDAR, Camera, IMU, GNSS-RTK, Wheel Odometry, Visual Odometry) to guarantee the redundancy and accuracy of the localization system. Moreover, maps will be integrated to robustify the performance and ensure safety by providing geometrical information about the transitioning structures. Furthermore, deep learning will be utilized to understand the operational conditions by labeling indoor and outdoor areas. This information will be encoded in maps to provide robots with expected operational conditions in advance and beyond the current sensing state. Accordingly, this self-awareness capability will be incorporated into the fusion process to control and switch between the localization techniques to achieve accurate and smooth IO-transitions, e.g., GNSS-RTK will be deactivated during the transition. As an urgent and unique demand to have an accurate and continuous IO-transition towards fully autonomous navigation/transportation, Saxion University and the proposal’s partners are determined to design a commercial and modular industrial-based localization system with robust performance, self-awareness about the localization capabilities and less human interference. Furthermore, AlFusIOn will intensively collaborate with MAPS (a RAAKPRO proposed by HAN University) to achieve accurate localization in outdoor environments.
The CARTS (Collaborative Aerial Robotic Team for Safety and Security) project aims to improve autonomous firefighting operations through an collaborative drone system. The system combines a sensing drone optimized for patrolling and fire detection with an action drone equipped for fire suppression. While current urban safety operations rely on manually operated drones that face significant limitations in speed, accessibility, and coordination, CARTS addresses these challenges by creating a system that enhances operational efficiency through minimal human intervention, while building on previous research with the IFFS drone project. This feasibility study focuses on developing effective coordination between the sensing and action drones, implementing fire detection and localization algorithms, and establishing parameters for autonomous flight planning. Through this innovative collaborative drone approach, we aim to significantly improve both fire detection and suppression capabilities. A critical aspect of the project involves ensuring reliable and safe operation under various environmental conditions. This feasibility study aims to explore the potential of a sensing drone with detection capabilities while investigating coordination mechanisms between the sensing and action drones. We will examine autonomous flight planning approaches and test initial prototypes in controlled environments to assess technical feasibility and safety considerations. If successful, this exploratory work will provide valuable insights for future research into autonomous collaborative drone systems, currently focused on firefighting. This could lead to larger follow-up projects expanding the concept to other safety and security applications.
In INCEPTION (INdustrial roboChEmic PlaTform ImplementatiON) Zuyd Hogeschool, the Noël Research Group (University of Amsterdam) and partners will develop and implement automated robotic platforms relying on advanced AI algorithms to accelerate reaction optimization, based on the RoboChem platform. Thus, synthesis of active pharmaceutical ingredients within the drug development process will be optimized. This will diminish the time-to-market for new medicines and improve the sustainability of this development process. To develop and implement these RoboChemic platforms, a consortium of chemical and high-tech partners will cover all aspects related to required hard and software, e.g. automation (Beartree Automation), reactors (Chemtrix) and analysis (Mettler-Toledo). The development and implementation will be guided by pharmaceutical Contract Research Organization end-users Ardena and Symeres. The mix of partners from academia (Noël Research Group), Center of Expertise CHILL, Zuyd and multiple companies ensures an efficient and integrated development. The overarching question: “How can AI-assisted optimization and RoboChemic platforms efficiently be implemented in the chemical industry?” and research question: “What improvements on set-ups, programs, and capabilities are necessary for optimal industrial use?” will be answered by: i) Extending the applicability of RoboChemic platforms in industry by exploiting the modularity of its hardware control platform by incorporating additional equipment, and exploiting its software flexibility by adding optimization objectives and human-in-the-loop functionalities. (WP2) ii) Exploring identification of pharmaceutical relevant side-products, and subsequent rapid optimization. (WP3) iii) Enabling efficient upscaling using data at small scale by coupled learning at higher scales. (WP4) iv) Allowing enzymatic catalyst screening by an automated platform. (WP4) v) Accelerating uptake of RoboChemic platforms in industry by dissemination of demonstrator applications and by evaluating a prospective start-up implementing and servicing RoboChemic platforms. (WP5) Thus, by implementing RoboChemic platforms in industry we will make the pharmaceutical CROs and equipment companies more competitive.
