Project description
Cognitive Systems and Robotics
Self-learning robotic ecology
http://www.fp7rubicon.eu/(opens in new window)" target="_blank">RUBICON will create a self-learning robotic ecology, consisting of a network of sensors, effectors and mobile robot devices. Enabling robots to seamlessly operate as part of these ecologies is an important challenge for robotics R&D, in order to support applications such as ambient assisted living, security and so on. Current approaches heavily rely on models of the environment and on human configuration/supervision and lack the ability to smoothly adapt to evolving situations. RUBICON ecology will be able to teach itself about its environment and learn to improve the way it carries out different tasks. The project will reduce the amount of preparation and pre-programming that robotic and/or wireless sensor network solutions require when they are deployed. In addition, RUBICON ecologies will reduce the need to maintain and re-configure already-deployed systems.
This project will create a self-learning robotic ecology, called RUBICON (for Robotic UBIquitous COgnitive Network), consisting of a network of sensors, effectors and mobile robot devices.
Enabling robots to seamlessly operate as part of these ecologies is an important challenge for robotics R&D, in order to support applications such as ambient assisted living, security, etc.
Current approaches heavily rely on models of the environment and on human configuration and supervision and lack the ability to smoothly adapt to evolving situations. These limitations make these systems hard and costly to deploy and maintain in real world applications, as they must be tailored to the specific environment and constantly updated to suit changes in both the environments and in the applications where they are deployed.
A RUBICON ecology will be able to teach itself about its environment and learn to improve the way it carries out different tasks. The ecology will act as a persistent memory and source of intelligence for all its participants and it will exploit the mobility and the better sensing capabilities of the robots to verify and provide the feedback on its own performance.
As the nodes of a RUBICON ecology will mutually support one another's learning, the ecology will identify, commission and fulfil tasks more effectively and efficiently.
The project builds on many years of experience across a world-leading consortium. It combines robotics, multi-agent systems, novelty detection, dynamic planning, statistical and computational neuroscience methods, efficient component & data abstraction, robot/WSN middleware and three robotic test-beds. Validation will take place using two application scenarios
Impact: The project will reduce the amount of preparation and pre-programming that robotic and/or wireless sensor network (WSN) solutions require when they are deployed. In addition, RUBICON ecologies will reduce the need to maintain and re-configure already-deployed systems, so that changes in the requirements of such systems can be easily implemented and new components can be easily accommodated.
The relative intelligence and mobility of a robot, when compared to those of a typical wireless sensor node, means that WSN nodes embedded in a RUBICON ecology can learn about their environment and their domain application, through the 'training' that is provided by the robot. This means that the quality of service which is offered by WSNs can be significantly improved, without the need for extensive human involvement.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- natural sciences biological sciences ecology
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering sensors smart sensors
- natural sciences biological sciences neurobiology computational neuroscience
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering robotics
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
FP7-ICT-2009-6
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Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Coordinator
4 Dublin
Ireland
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.