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FAME

Financiado

FAME: OPEN-ENDED MANIPULATION TASK LEARNING WITH FAME (FUTURE-ORIENTED COGNITIVE...

FAME: OPEN-ENDED MANIPULATION TASK LEARNING WITH FAME (FUTURE-ORIENTED COGNITIVE1 ACTION MODELLING ENGINE) The realization of computational models for accomplishing everyday manipulation tasks for any object and any purpose would be a disruptive breakthrough in the creation of versatile, general-purpose robot agents; and it is a grand... ver más

31/08/2028

UBREMEN

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

UNIVERSITAET BREMEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-06-20

ERC-2022-ADG: ERC ADVANCED GRANTS Scope:Objectives

I+D

Cerrada hace 2 años

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1 Participantes

UBREMEN

2.50M€ | Lider

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Duración del proyecto: 62 meses Fecha Inicio: 2023-06-20
Fecha Fin: 2028-08-31

Líder del proyecto

UNIVERSITAET BREMEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Descripción del proyecto The realization of computational models for accomplishing everyday manipulation tasks for any object and any purpose would be a disruptive breakthrough in the creation of versatile, general-purpose robot agents; and it is a grand challenge for AI and robotics. Humans are able to accomplish tasks such as cut up the fruit for many types of fruit by generating a large variety of context-specific manipulation behaviors. They can typically accomplish the tasks on the first attempt despite uncertain physical conditions and novel objects. Acting so effectively requires comprehensive reasoning about the possible consequences of intended behavior before physically interacting with the real world. In the FAME project, I will investigate the research hypothesis that a knowledge representation and reasoning (KR&R) framework based on explictly-represented and machine-interpretable inner-world models can enable robots to contextualize underdetermined manipulation task requests on the first attempt. To this end, I will design, implement, and evaluate FAME (Future-oriented cognitive Action Modelling Engine), a hybrid symbolic/subsymbolic KR&R framework that will contextualize actions by reasoning symbolically in an abstract and generalized manner but also by reasoning with one’s eyes and hands through mental simulation and imagistic reasoning. Realizing FAME requires three breakthrough research results: (1) modelling and parameterization of manipulation motion patterns and understanding the resulting effects under uncertain conditions; (2) the ability to mentally simulate imagined and observed manipulation tasks to link them to the robot’s knowledge and experience; and (3) the on-demand acquisition of task-specific causal models for novel manipulation tasks through mental physics-based simulations. To assess the power and feasibility of FAME, I will use open manipulation task learning as a benchmark challenge.

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