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“Do AI ethics in design, not just for legal compliance”: Takeaways from RECLAIM open training on trustworthy AI, Circular Economy 

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To this end, the RECLAIM consortium organised an open training workshop last month on trustworthy Artificial Intelligence (AI) and Circular Economy. The objective behind the workshop was to present the latest developments in AI, Robotics, and socially sensitive gaming, and how these can support circular economy and Trustworthy AI.   The training program invited specialists in […]

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Bridging Distances: Advanced Material Recovery in Remote Areas!

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The RECLAIM project, funded by the EU’s Horizon Europe program, aims to offer innovative solutions in the waste management industry in regions lacking material recovery facilities. By integrating cutting-edge AI, data, and robotic technologies, the project develops the first portable, robotic Material Recovery Facility (prMRF). This innovative solution enables material recovery in remote and geographically

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“New grippers for better pick ratio; seeking clustering options with EU projects”: RECLAIM General Assembly ends on a high note  

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The RECLAIM Project recently concluded its second General Assembly hosted by consortium partner KU Leuven at their Department of Mechanical Engineering campus in the heritage city of Leuven, Belgium.

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Milestone Moment: RECLAIM Successfully Integrates Material Recovery Systems Inside PrMRF

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RECLAIM is a portable material recovery facility packed in a box but we are actually thinking outside of it! The last 18 months have been an intense test-bed of expending energy on experimentation and implementation of our solution at RECLAIM project. While we made good use of robotics, grippers, advanced imaging and artificial intelligence to

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The INC-4 is underway in Toronto, Canada.

INC-4 on Plastic Pollution underway and how RECLAIM’s prMRF aims to deliver on Circular Economy 

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The fourth meeting of the Intergovernmental Negotiating Committee (INC) to develop an international legally binding instrument on plastic pollution, including in the marine environment (Plastic Pollution INC-4), is underway in Canada. What happens here will advance the draft global treaty towards its final version, ensuring that the stage is set for ending plastic pollution in the next session (INC-5) in Busan, Korea. At RECLAIM Project, we wholeheartedly support this instrument but are also taking active steps to be the solution to the plastic problem.

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Enhanced Material Recovery for Europe’s Circular Economy Goals

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To enhance circularity in Europe, the European Environmental Agency’s 2024 report titled “Accelerating the Circular Economy in Europe” calls for a systemic shift in waste management to improve recyclate quality. RECLAIM’s prMRF aims to boost both quantity and quality of recycled materials, aligning with the Green Deal goals. 

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Citizen Science: How Big Plastic Count, RECLAIM’s Recycling Data Game use this approach to tackle plastic waste

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Mobilizing citizen science can play a pivotal role, generating data, raising awareness, engaging communities and driving policy action.  Urgent actions by multiple stakeholders are needed across the plastic value chain to tackle plastic pollution, addressing both the production of plastic and sustainable management of waste. One crucial step could be mobilising citizen science as a

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RECLAIM: Efficient Material Recovery Everywhere

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The RECLAIM project explores new, more innovative solutions, needed to reduce costs and improve waste treatment efficiency even further. (Published in Waste Management World). Despite the success of large-scale MRF facilities, there are cases where new, more innovative solutions are needed to reduce costs and improve waste treatment efficiency even further. The RECLAIM project explores

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RECLAIM game: role and function in the project.

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The RECLAIM recycling game will allow and encourage citizens to participate in the research and development activities of the project.  IntroductionThe recycling data-game (RDG), which is being developed in the context of the RECLAIM project, aims at supplementing and supporting the training and optimization of AI algorithms for the material identification, segmentation and categorization module

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