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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Autonomously exploring and mapping is one of the open challenges of robotics and artificial intelligence. Especially when the environments are unknown, choosing the optimal navigation directive is not straightforward. In this paper, we propose a reinforcement learning framework for navigating, exploring, and mapping unknown environments. The reinforcement learning agent is in charge of selecting the commands for steering the mobile robot, while a SLAM algorithm estimates the robot pose and maps the environments. The agent, to select optimal actions, is trained to be curious about the world. This concept translates into the introduction of a curiosity-driven reward function that encourages the agent to steer the mobile robot towards unknown and unseen areas of the world and the map. We test our approach in explorations challenges in different indoor environments. The agent trained with the proposed reward function outperforms the agents trained with reward functions commonly used in the literature for solving such tasks.
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Professional development of teacher educators is an important topic, because teacher educators need to maintain and enhance their expertise in order to educate our future teachers (Kools & Koster, n.d. ; Dengerink, Lunenberg & Kools, 2015). How do teacher educators fulfil this task, especially within the hectic timeframe of everyday work? I asked four colleges to participate in a group to share their experiences, actions or behaviour in the organisation about their development in their profession of being a teacher educator. My purpose is to bring awareness and movement into that group. My research focusses on teacher educators in a large teacher education department in the Netherlands and the opportunities for action available to them. During this study we are currently creating a learning environment in which mutual cooperation increases the learning potential of all participants. In this group participants take or make time to learn, giving words to their scopes . Researcher and participants discuss and explore on the basis of equality, reciprocity and mutual understanding. By deploying methods borrowed from ‘Appreciative Inquiry’(Massenlink et al., 2008) the enthusiasm of a study group is raised and the intrinsic motivation of the participants stimulated. Our study group will convene three times. Its goal is to stimulate cooperation among teacher educators through optimisation of existing qualities, a method that could be described as empowerment, or a process of collective reinforcement ‘To learn’ involves experiencing that what one does really matters, as well as developing one’s own persona in the local community. Intervention, action, reflection and study group meetings alternate in the course of our research. In addition to audio and video recordings, data consists of reports drawn up on the basis of member checks. Data is analysed qualitatively by coding the interview texts and reports. After applying the codes, the researcher discusses the coding in a research group and with the participants of the study group (membercheck). Working collaboratively can offer learning challenges that catalyse growth as a professional, teacher educators become acquainted and approach each other from the perspective of their respective professional and functional responsibilities. This study offers perspectives for other teacher educators to recognize these possibilities in their own situation. Moreover the study offers a description of a way to organise collegial exchange. The research is related to the RDC professional development of teacher educators.
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In het kader van het Hoogwaterbeschermingsprogramma (HWBP) neemt de vraag naar klei voor het versterken van dijken toe, echter is het aanbod beperkt. Dit voorstel richt zich op ontwikkelen van nieuwe duurzame en kosteneffectieve technieken die het mogelijk maken om zout sediment uit estuaria in te kunnen zetten voor de dijkversterkingsopgave. Gebiedseigen materiaal, met name het zoute slib, kan worden ingezet voor klei productie in lokale dijkverzwaring en draagt bij aan duurzaam grondstoffenverbruik, klimaatadaptatie en de ecologische kwaliteit van estuaria. Met het project “Ontzouten rijpend slib voor Deltabescherming” gaan het lectoraat Sustainable River Management van de HAN, Ecoshape, Netics in samenwerking met partijen verenigd in het interbestuurlijk project IBP-VLOED onderzoeken hoe zout slib (kosten)effectief kan worden ontdaan van het zout, zodat het gebruikt kan worden in de regionale dijkversterkingsopgave. In IBP-Vloed zijn alle relevante nationale en regionale (semi)overheden, kennisinstellingen en belangenorganisaties vertegenwoordigd die zich richten op hergebruik van slib uit het Eems-Dollard estuarium. Beoogd wordt om een geschikte kosteneffectieve en schaalbare ontzoutingsmethode (strategie) te ontwikkelen die rekening houdt met de samenhang van de governing parameters en de heterogeniteit in samenstelling en structuur van het zoute slib uit estuaria zoals het Eems-Dollard gebied. De resultaten worden gepresenteerd tijdens een workshop en gebundeld in de vorm van best practices.
