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CoreSense

Financiado

Cerrado

CoreSense A Hybrid Cognitive Architecture for Deep Understanding

Cognitive robots are augmenting their autonomy, enabling them to deployments in increasingly open-ended environments. This offers enormous possibilities for improvements in human economy and wellbeing. However, it also poses stron... ver más

30/09/2026

UPM

5M€

Presupuesto del proyecto: 5M€

Líder del proyecto

UNIVERSIDAD POLITÉCNICA DE MADRID No se ha especificado una descripción o un objeto social para esta compañía.

Total investigadores 3947

Fecha límite participación Sin fecha límite de participación.

Ver 7 Participantes

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-06-13

HORIZON-CL4-2021-DIGITAL-EMERGING-01-11: Pushing the limit of robotics cogni... Expected Outcome:Proposal results are expected to contribute to the following expected outcome:

I+D

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

7 Participantes

UPM

1.19M€ | Lider

PAL ROBOTICS

448.75K€ | Participante

IRISH MANUFACTURING RESEARCH

787.91K€ | Participante

Fraunhofer

822.47K€ | Participante

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Duración del proyecto: 51 meses Fecha Inicio: 2022-06-13
Fecha Fin: 2026-09-30

Líder del proyecto

UNIVERSIDAD POLITÉCNICA DE MADRID No se ha especificado una descripción o un objeto social para esta compañía.

Total investigadores 3947

Presupuesto del proyecto 5M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Cognitive robots are augmenting their autonomy, enabling them to deployments in increasingly open-ended environments. This offers enormous possibilities for improvements in human economy and wellbeing. However, it also poses strong risks that are difficult to assess and control by humans. The trend towards increased autonomy conveys augmented problems concerning reliability, resilience, and trust for autonomous robots in open worlds. The essence of the problem can be traced to robots suffering from a lack of understanding of what is going on and a lack of awareness of their role in it. This is a problem that artificial intelligence approaches based on machine learning are not addressing well. Autonomous robots do not fully understand their open environments, their complex missions, their intricate realizations, and the unexpected events that affect their performance. An improvement in the capability to understand of autonomous robots is needed. This project tries to provide a solution to this need in the form of 1) a theory of understanding, 2) a theory of awareness, 3) reusable software assets to apply these theories in real robots, and 4) three demonstrations of its capability to a) augment resilience of drone teams, b) augment flexibility of manufacturing robots, and c) augment human alignment of social robots. In summary, we will develop a cognitive architecture for autonomous robots based on a formal concept of understanding, supporting value-oriented situation understanding and self-awareness to improve robot flexibility, resilience and explainability.

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