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HUMOUR

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HUman behavioral Modeling for enhancing learning by Optimizing hUman Robot inter...

HUman behavioral Modeling for enhancing learning by Optimizing hUman Robot interaction The HUMOUR project will investigate and develop efficient robot strategies to facilitate the acquisition of motor skills. We will address both the (human) trainee and the (robot) trainer sides, by combining behavioural studies on... ver más

31/12/2011

IIT

3M€

Presupuesto del proyecto: 3M€

Líder del proyecto

FONDAZIONE ISTITUTO ITALIANO DI TECNOLOGIA No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 7 Participantes

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2011-12-31 No tenemos la información de la convocatoria

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Características del participante

Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

Información adicional privada

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

7 Participantes

IIT

0.00€ | Lider

IFADO

N.D. | Participante

AAU

N.D. | Participante

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Líder del proyecto

FONDAZIONE ISTITUTO ITALIANO DI TECNOLOGIA No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 3M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The HUMOUR project will investigate and develop efficient robot strategies to facilitate the acquisition of motor skills. We will address both the (human) trainee and the (robot) trainer sides, by combining behavioural studies on motor learning and its neural correlates with design, implementation, and validation of robot agents that behave as 'optimal' trainers, which efficiently exploit structure and plasticity of the human sensorimotor systems. On the human trainee side, we will focus on the cognitive and neural mechanisms underlying the acquisition of a variety of motor skills, by specifically aiming at understanding the way humans physically cooperate in acquiring a motor skill and how physical assistance affects motor learning. Experiments will enable us to identify determinants and dynamics of the learning process in representative motor tasks, and will provide the foundations for designing efficient schemes of assistance. On the robot trainer side, we will develop several robot agents for the acquisition of a variety of motor skills. They will be capable of generating appropriate schemes of assistance, based on the specific task needs and possibly learned from human experts. Robots will continuously adapt assistance, in terms of the observed performance and of neural and cognitive correlates of adaptation, to the specific user (e.g. patient) and his or her state. Robot trainers will be validated in the context of motor skill learning and robot-assisted rehabilitation. Robot agents that facilitate the capture of new motor skills may potentially benefit large groups of individuals, by helping professionals, e-g- surgeons, to acquire delicate motor skills; by providing older persons greater access to activities like fitness, sports, and arts, thus ultimately improving their quality of life. Furthermore, more effective training agents would also represent an innovative approach to robot therapy, which would likely increase its impact and extend its scope.

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