CORESENSE is a Horizon Europe funded project that aims to develop a theory and a derived cognitive architecture for understanding in autonomous robots. The project will provide a solution to the challenges of reliability, resilience, and trust in autonomous robots by creating a theory of understanding and awareness and reusable software assets. The project will demonstrate its capability in augmenting the resilience of drone teams, the flexibility of manufacturing robots, and the human alignment of social robots.

The goal is to improve the capability of autonomous robots to understand their open environments and complex missions, leading to improved flexibility, resilience, and explainability.

CORESENSE Consortium

The CORESENSE project is a collaboration between leading research institutions and companies from across Europe. Our partners include the Universidad Politécnica de Madrid (Spain), Technische Universiteit Delft (Netherlands), Fraunhofer IPA (Germany), Universidad Rey Juan Carlos (Spain), PAL Robotics (Spain), and Irish Manufacturing Research (Ireland). Each partner brings a unique set of expertise and experience to the project, ensuring a multidisciplinary approach to the development of the cognitive architecture for autonomous robots. With a strong and diverse team of partners, Coresense is poised to make significant advancements in the field of autonomous robotics.

CORESENSE Objectives?

CoreSense project aims to develop a theory of understanding and awareness for autonomous robots, which will be implemented as a reference architecture and engineering toolbox. This theory and technology will be used in building autonomous robots with increased capabilities to work without/with limited supervision and improved intuitive, safe and efficient cognitive, social, and physical capabilities. The project also aims to create an open-source community in the ROS ecosystem around the software developed in the project. The core of the project is the development of a hybrid cognitive architecture that uses knowledge as the core substrate for its operation and is self-aware, trustworthy, model-centric and value-oriented.

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Explore our latest blog article: SymFormer, End-to-End Symbolic Regression Using Transformer-Based Architecture - work from our partner @CIIRCCTU, supported by #CORESENSE

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#HorizonEurope #AI #robots #software #innovation #ROS

In WP2 #CORESENSE is developing a reference architecture for #RobotAwareness, comprising a runtime system, understanding core, and awareness system, to enhance the autonomy and reliability of #robots in open environments👉https://coresense.eu/
#HorizonEurope #AI #ROS #software

Irene presented our join paper with @RoboticaUnileon at ICARSC, related to our work at @CORESENSE_EU


Did you know that @urjc delivered a lecture in ‘Formalizing Robotics Competitions: a Practical Case for the RoboCup@Home Challenge’ at #ICARSC2024?

Dive deeper into this topic at https://easychair.org/publications/preprint/tjmr

#CORESENSEproject #HorizonEurope #Robots #SocialRobots #ROS #AI


Discover the theoretical foundation of the #CORESENSEproject: Work Package 1 focuses on formalizing the theory of understanding and awareness, laying the groundwork for innovative advancements in #AutonomousRobotics.
#HorizonEurope #AI #robots #robotics #software #innovation #ROS

We attended the Plenary Meeting of the #EU #project @CORESENSE_EU. It was a pleasure to meet up with the project's #partners to discuss the current development made in the project and to have an overview of the #future steps.
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  • Enhancing Cognitive Robotics: PAL Robotics in the CORESENSE Project
    The CORESENSE project is driven by the ambition to advance the cognitive abilities of robots, making them more perceptive and capable of understanding complex environments. This EU initiative focuses on integrating sophisticated cognitive functions into robotic systems, aiming to improve their autonomy and interaction with humans. PAL Robotics participation is strategically focused on employing their…
  • SymFormer: End-to-End Symbolic Regression Using Transformer-Based Architecture
    We present you SymFormer, a transformer-based symbolic regression approach. Symbolic regression is a method that automatically generates models as analytic free-form formulas from data. Symbolic regression has been successfully used in many nonlinear modeling tasks with quite impressive results. Traditionally, evolutionary methods like genetic programming have been used for symbolic regression, but they suffer from…
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