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.
Purpose: This paper aims to define the influence of the physical and social dimensions of the work environment on knowledge productivity of academics in Dutch Universities of Applied Sciences.Design/methodology/approach: Literature review; a multiple case study based on literature review (6 cases); a survey (n=188).Findings: Knowledge workers share two basic needs: their productivity requires isolation (internalization of knowledge) and interaction (externalization of knowledge), supported by different spatial concepts. None of the work environments involved in the study adequately support all of the phases in the knowledge development process adequately. Collective productivity is primarily determined by the physical dimension of the workplace; whereas the social dimension is crucial for personal productivity. Social interaction has a stronger effect than distraction; and the layout has a stronger effect than comfort.Conclusions - A high performance workplace supports both externalization and internalization of knowledge, allowing group members to collaborate and communicate according to need. More traditional work environments support internalization; innovative workplace designs (the office as meeting place) are more suited to support interaction and collaboration. Discover why freedom of choice is the key.Recommendations - Academics should be allowed to choose as to how, where and when they work and involved during the development of new concepts.Paper type: Research paper
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Educational institutions and vocational practices need to collaborate to design learning environments that meet current-day societal demands and support the development of learners’ vocational competence. Integration of learning experiences across contexts can be facilitated by intentionally structured learning environments at the boundary of school and work. Such learning environments are co-constructed by educational institutions and vocational practices. However, co-construction is challenged by differences between the practices of school and work, which can lead to discontinuities across the school–work boundary. More understanding is needed about the nature of these discontinuities and about design considerations to counterbalance these discontinuities. Studies on the co-construction of learning environments are scarce, especially studies from the perspective of representatives of work practice. Therefore, the present study explores design considerations for co-construction through the lens of vocational practice. The study reveals a variety of discontinuities related to the designable elements of learning environments (i.e. epistemic, spatial, instrumental, temporal, and social elements). The findings help to improve understanding of design strategies for counterbalancing discontinuities at the interpersonal and institutional levels of the learning environment. The findings confirm that work practice has a different orientation than school practice since there is a stronger focus on productivity and on the quality of the services provided. However, various strategies for co-construction also seem to take into account the mutually beneficial learning potential of the school–work boundary.
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The European creative visual industry is undergoing rapid technological development, demanding solid initiatives to maintain a competitive position in the marketplace. AVENUE, a pan-European network of Centres of Vocational Excellence, addresses this need through a collaboration of five independent significant ecosystems, each with a smart specialisation. AVENUE will conduct qualified industry-relevant research to assess, analyse, and conclude on the immediate need for professional training and educational development. The primary objective of AVENUE is to present opportunities for immediate professional and vocational training, while innovating teaching and learning methods in formal education, to empower students and professionals in content creation, entrepreneurship, and innovation, while supporting sustainability and healthy working environments. AVENUE will result in a systematised upgrade of workforce to address the demand for new skills arising from rapid technological development. Additionally, it will transform the formal education within the five participating VETs, making them able to transition from traditional artistic education to delivering skills, mindsets and technological competencies demanded by a commercial market. AVENUE facilitates mobility, networking and introduces a wide range of training formats that enable effective training within and across the five ecosystems. A significant portion of the online training is Open Access, allowing professionals from across Europe to upgrade their skills in various processes and disciplines. The result of AVENUE will be a deep-rooted partnership between five strong ecosystems, collaborating to elevate the European industry. More than 2000 professionals, employees, students, and young talents will benefit from relevant and immediate upgrading of competencies and skills, ensuring that the five European ecosystems remain at the forefront of innovation and competitiveness in the creative visual industry.
Studenten in het beroepsonderwijs leren op de werkplek om een goede beroepsuitoefenaar te worden. Beoordeling van het werkplekleren gebeurt vaak op de werkplek en door de werkplek. Dit promotieonderzoek wil in kaart brengen hoe werkplekopleiders de student beoordelen.
Collaborative networks for sustainability are emerging rapidly to address urgent societal challenges. By bringing together organizations with different knowledge bases, resources and capabilities, collaborative networks enhance information exchange, knowledge sharing and learning opportunities to address these complex problems that cannot be solved by organizations individually. Nowhere is this more apparent than in the apparel sector, where examples of collaborative networks for sustainability are plenty, for example Sustainable Apparel Coalition, Zero Discharge Hazardous Chemicals, and the Fair Wear Foundation. Companies like C&A and H&M but also smaller players join these networks to take their social responsibility. Collaborative networks are unlike traditional forms of organizations; they are loosely structured collectives of different, often competing organizations, with dynamic membership and usually lack legal status. However, they do not emerge or organize on their own; they need network orchestrators who manage the network in terms of activities and participants. But network orchestrators face many challenges. They have to balance the interests of diverse companies and deal with tensions that often arise between them, like sharing their innovative knowledge. Orchestrators also have to “sell” the value of the network to potential new participants, who make decisions about which networks to join based on the benefits they expect to get from participating. Network orchestrators often do not know the best way to maintain engagement, commitment and enthusiasm or how to ensure knowledge and resource sharing, especially when competitors are involved. Furthermore, collaborative networks receive funding from grants or subsidies, creating financial uncertainty about its continuity. Raising financing from the private sector is difficult and network orchestrators compete more and more for resources. When networks dissolve or dysfunction (due to a lack of value creation and capture for participants, a lack of financing or a non-functioning business model), the collective value that has been created and accrued over time may be lost. This is problematic given that industrial transformations towards sustainability take many years and durable organizational forms are required to ensure ongoing support for this change. Network orchestration is a new profession. There are no guidelines, handbooks or good practices for how to perform this role, nor is there professional education or a professional association that represents network orchestrators. This is urgently needed as network orchestrators struggle with their role in governing networks so that they create and capture value for participants and ultimately ensure better network performance and survival. This project aims to foster the professionalization of the network orchestrator role by: (a) generating knowledge, developing and testing collaborative network governance models, facilitation tools and collaborative business modeling tools to enable network orchestrators to improve the performance of collaborative networks in terms of collective value creation (network level) and private value capture (network participant level) (b) organizing platform activities for network orchestrators to exchange ideas, best practices and learn from each other, thereby facilitating the formation of a professional identity, standards and community of network orchestrators.
