From the pubisher's website: This paper aims to chart the (moral) values from a robotic industry's perspective regarding the introduction of robots in education. To our knowledge, no studies thus far have addressed this perspective in considering the moral values within this robotic domain. However, their values could conflict with the values upheld by other relevant stakeholders, such as the values of teachers, parents or children. Hence, it is crucial to take the various perspectives of relevant stakeholder's moral values into account. For this study, multiple focus group sessions (n=3) were conducted in The Netherlands with representatives (n=13) of robotic companies on their views of robots in primary education. Their perceptions in terms of opportunities and concerns, were then linked to business values reported in the extant literature. Results show that out of 26 business values, mainly six business values appeared relevant for robot tutors: 1) profitability, 2) productivity, 3 & 4) innovation and creativity, 5) competitiveness, and 6) risk orientation organization. https://doi.org/10.1109/DEVLRN.2019.8850726
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The number of applications in which industrial robots share their working environment with people is increasing. Robots appropriate for such applications are equipped with safety systems according to ISO/TS 15066:2016 and are often referred to as collaborative robots (cobots). Due to the nature of human-robot collaboration, the working environment of cobots is subjected to unforeseeable modifications caused by people. Vision systems are often used to increase the adaptability of cobots, but they usually require knowledge of the objects to be manipulated. The application of machine learning techniques can increase the flexibility by enabling the control system of a cobot to continuously learn and adapt to unexpected changes in the working environment. In this paper we address this issue by investigating the use of Reinforcement Learning (RL) to control a cobot to perform pick-and-place tasks. We present the implementation of a control system that can adapt to changes in position and enables a cobot to grasp objects which were not part of the training. Our proposed system uses deep Q-learning to process color and depth images and generates an (Formula presented.) -greedy policy to define robot actions. The Q-values are estimated using Convolution Neural Networks (CNNs) based on pre-trained models for feature extraction. To reduce training time, we implement a simulation environment to first train the RL agent, then we apply the resulting system on a real cobot. System performance is compared when using the pre-trained CNN models ResNext, DenseNet, MobileNet, and MNASNet. Simulation and experimental results validate the proposed approach and show that our system reaches a grasping success rate of 89.9% when manipulating a never-seen object operating with the pre-trained CNN model MobileNet.
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Social robots have been introduced in different fields such as retail, health care and education. Primary education in the Netherlands (and elsewhere) recently faced new challenges because of the COVID-19 pandemic, lockdowns and quarantines including students falling behind and teachers burdened with high workloads. Together with two Dutch municipalities and nine primary schools we are exploring the long-term use of social robots to study how social robots might support teachers in primary education, with a focus on mathematics education. This paper presents an explorative study to define requirements for a social robot math tutor. Multiple focus groups were held with the two main stakeholders, namely teachers and students. During the focus groups the aim was 1) to understand the current situation of mathematics education in the upper primary school level, 2) to identify the problems that teachers and students encounter in mathematics education, and 3) to identify opportunities for deploying a social robot math tutor in primary education from the perspective of both the teachers and students. The results inform the development of social robots and opportunities for pedagogical methods used in math teaching, child-robot interaction and potential support for teachers in the classroom
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The Hospitality, Tourism, Innovation & Technology Experts Network (HTIT-EN) is a pivotal initiative aimed at unlocking societal impact potential. The Dutch hospitality and tourism sector, which employs over half a million individuals and annually hosts more than 40 million guests, ranks as the Netherlands’ 8th largest economic sector. However, this sector faces numerous challenges, including the uncertain impact of emerging technologies and issues such as unethical behavior, workforce attrition, and staff shortages, which have been exacerbated by the COVID-19 pandemic. The advent of emerging technologies like service robots, immersive experiences, and artificial intelligence has brought the sector to a critical juncture. These innovations pose significant disruptions, challenging the traditional concept of hospitality and questioning the positive societal impact in terms of ethical considerations, inclusivity, affordability, and data privacy.Strategically positioned to address these challenges, HTIT-EN focuses on leveraging emerging technologies to create impactful scenarios and shape the future of hospitality and tourism. Our motivation stems from the sector’s societal importance and its continuous influence on our daily lives. By harnessing technology and innovation, we aim to tackle industry-specific issues and extend the positive societal impact to related human-centered service industries.The overarching mission of HTIT-EN is to empower the Dutch Hospitality and Tourism sector to serve as a driving force for technology-enabled societal impact. The primary objective is to align research activities and promote collaboration. Key objectives include bringing together leading professors specializing in technology-driven impact within the hospitality and tourism sector, initiating research projects in line with a shared research agenda and in collaboration with local and international industry partners, and collaboratively developing expertise in emerging technologies that empower the role of hospitality and tourism as catalysts for societal impact. This endeavor contributes to the development and acceleration of the Knowledge and Innovation Agenda (KIA) ‘Key technologies’ & ‘Digitalization’. The aim is to foster an excellent reputation for Dutch hospitality and tourism as a global leader in technology-driven societal impact.We have strong support from CELTH, the Centre of Expertise within the domain of leisure, tourism and hospitality for the overall ambitions of the research project.Societal issueThe HTIT-EN project bridges societal importance and cross-cutting issues in the tourism and hospitality sectors. It’s fueled by the ambition to leverage emerging technologies to tackle industry-specific challenges, including knowledge and skills gaps, labor shortages and replacements, and evolving consumer expectations.Benefit to societyThe platform brings together professors and researchers from MBO, HBO and WO knowledge institutes as well as diverse set of professional partners to stimulate collaboration, align research lines and establish joint a joint research agenda on how technology-driven impact may become a catalyst within hospitality and tourism.
Due to the exponential growth of ecommerce, the need for automated Inventory management is crucial to have, among others, up-to-date information. There have been recent developments in using drones equipped with RGB cameras for scanning and counting inventories in warehouse. Due to their unlimited reach, agility and speed, drones can speed up the inventory process and keep it actual. To benefit from this drone technology, warehouse owners and inventory service providers are actively exploring ways for maximizing the utilization of this technology through extending its capability in long-term autonomy, collaboration and operation in night and weekends. This feasibility study is aimed at investigating the possibility of developing a robust, reliable and resilient group of aerial robots with long-term autonomy as part of effectively automating warehouse inventory system to have competitive advantage in highly dynamic and competitive market. To that end, the main research question is, “Which technologies need to be further developed to enable collaborative drones with long-term autonomy to conduct warehouse inventory at night and in the weekends?” This research focusses on user requirement analysis, complete system architecting including functional decomposition, concept development, technology selection, proof-of-concept demonstrator development and compiling a follow-up projects.
Robots en cobots (collaborative robots) worden steeds meer ingezet voor het realiseren van machinebelading, begin- en end-of-line toepassingen en/of assemblagetaken. Typisch zijn er significante investeringen benodigd om binnen een MKB-bedrijf aan de slag te kunnen gaan met een robot- of cobotimplementatie. Binnen dit projectvoorstel wordt verkennend onderzoek uitgevoerd om te bepalen of, hoe en met welke randvoorwaarden een nieuwe business model op basis van pay-per-use /pay-per-pick productie/assemblage d.m.v. robots mogelijk is. Dit om eventuele directe investeringen weg te halen bij de MKB’er, maar op basis van wederzijds commitment te realiseren. Hierbij wordt bekeken of en hoe een pay-per-use model economisch en technisch haalbaar is en welke randvoorwaarden hierbij in acht genomen moeten worden. Op basis van een vooraf gedefinieerde use-case van het MKB bedrijf AKarton worden diverse technische concepten uitgedacht en een fysieke demonstrator gerealiseerd. Naast het technische aspect wordt vanuit de bedrijfskundige hoek geïnventariseerd aan welke technische-, organisatorische- en overige randvoorwaarden moet worden voldaan om een dergelijk pay-per-use dienstconcept economisch rendabel te kunnen realiseren (nieuw business model). De beoogde uitkomst van het project is een inschatting van technische- en economische haalbaarheid (verdienmodel/business model), de schaalbaarheid/methode van implementatie van dit product- en dienstconcept inclusief de in acht te nemen randvoorwaarden. De projectpartners in dit project zijn Fontys Engineering (FHENG) Fontys International Business School (FIBS), TMC en MKB bedrijf Akarton. FontysFIBS onderzoekt hierin de economische haalbaarheid, Fontys Engineering en TMC onderzoeken de technische haalbaarheid en Akarton levert de use case en valideert of het ontwikkelde verdienmodel bruikbaar is voor MKB ondernemingen
