What is CORESENSE?
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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Check out the Data in Brief paper published that details a dataset collected during the #RoboCup 2023-2024 using the TIAGo #robot https://coresense.eu/robocup-2023-and-2024-dataset-a-resource-for-autonomous-robotics/
This plays a crucial role in evaluating and testing developments from #CoreSense within the social robotics #testbed.
In article https://coresense.eu/a-survey-of-ontology-enabled-processes-for-dependable-robot-autonomy/ we summarize recent studies that employ knowledge-based methods to enhance robot autonomy. One of the main goals is to conduct a systematic analysis of recent and relevant projects that use ontologies for autonomous robots.
#Coresense @La_UPM
Our partner @urjc at #ROSCon Seville. Great opportunity to connect with the #ROS community, discuss the use of ROS in projects, research and robots, and contribute to workshops and presentations related to #Coresense. 🔗https://coresense.eu/coresense-at-roscon-2024/ @Fmrico #HorizonEurope #Robots
2 Last week’s Consortium Meeting was a success! If we had to choose one thing that recaps it, it would be the strong team collaboration 🔗 https://coresense.eu/coresense-consortium-meeting-in-mullingar-a-collaborative-success
#Coresense #HorizonEurope #Robots #AI #Event @urjc @IMR_ie @Fraunhofer_IPA @tudelft @PALRobotics @La_UPM
- RoboCup 2023 and 2024 Dataset: A Resource for Autonomous Robotics
We are pleased to announce Data in Brief recently published a paper of a dataset collected during the RoboCup 2023 and 2024 competitions using the TIAGo robot. This dataset includes ROSbag files containing the robot’s sensor data, as well as external videos documenting the tests. Key Highlights: This dataset is also a crucial resource for evaluating and… - A survey of ontology-enabled processes for dependable robot autonomyAutonomous robots are currently active across various fields, executing complex tasks. While their use in dynamic environments holds limitless potential, unresolved challenges in reliability and trust still pose significant risks. This article, which correspondence author is Esther Aguado (UPM), offers a summary of prominent and recent studies that employ knowledge-based methods to enhance robot autonomy.…
