ADR Awareness Centre

The European AI, Data and Robotics stakeholders operate from diverse historic backgrounds. To tap into the potentials of the diverse knowledge and resources generated in the AI, Data, and Robotics ecosystem, an all-inclusive collaboration approach between these stakeholders is necessary to make the ADR resources available and accessible to all.

For this, the ADR Awareness Centre is developed as an open repository of ADR educational resources and materials to enable collaboration and alignment among all projects within the Partnership, relevant external projects, and the public. The Awareness Centre will collect, validate, and publish educational resources and materials related to AI, Data, and Robotics.

- Who is this for? Everyone. Every project and organization in Europe including the general member of the public
- What does this Centre provide? AI, Data, and Robotics educational resources and materials produced in the EU
- When a new resource becomes available? The list of the resources will be updated when a new resource is submitted and validated
- Why should I publish a resource? Firstly, publishing your materials will promote and make them accessible to all. Secondly, it increases the transparency of your work and improves your and your organizations reputation. Last but not the least, your material will help address ADR challenges and contribute to shaping the future of ADR in Europe.
- How can I contribute and publish my resources? The Centre designed a very simple process for you to submit and publish your resources. You only need to submit your resource here and we will review and get it published and publicly accessible to all.
- What kinds of resources can I publish? The Centre only collects and publishes ADR related educational resources that can be publicly available and accessible to all. Copyrighted and licensed contents will not be published in the Centre.

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Non-Markov Decision Processes and Reinforcement Learning

We present non-Markov decision processes, where rewards and dynamics can depend on the history of events. This is contrast with Markov Decision Processes, where the dependency is limited to the last state and action.

Augmentation-free unsupervised approach for point clouds

Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods.

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ADR Awareness Centre

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Target Audience

Type of Educational Resource

Non-Markov Decision Processes and Reinforcement Learning

Augmentation-free unsupervised approach for point clouds

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