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Robots with animal like resilience

Despite over 50 years of research in robotics, most existing robots are far from being as resilient as the simplest animals: they are fragile machines that easily stop functioning in difficult conditions. The goal of this proposal... ver más

31/10/2020

INRIA

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQU... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 2 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2020-10-31

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-StG-2014: ERC Starting Grant

I+D

Cerrada hace 10 años

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2 Participantes

INRIA

1.50M€ | Lider

NEOKER

400.00K€ | Participante

Últimas noticias

30-11-2024: Cataluña Gestión For... Se ha cerrado la línea de ayuda pública: Gestión Forestal Sostenible para Inversiones Forestales Productivas para el organismo:

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

Duración del proyecto: 66 meses Fecha Inicio: 2015-04-24
Fecha Fin: 2020-10-31

Presupuesto del proyecto 1M€

Descripción del proyecto Despite over 50 years of research in robotics, most existing robots are far from being as resilient as the simplest animals: they are fragile machines that easily stop functioning in difficult conditions. The goal of this proposal is to radically change this situation by providing the algorithmic foundations for low-cost robots that can autonomously recover from unforeseen damages in a few minutes. The current approach to fault tolerance is inherited from safety-critical systems (e.g. spaceships or nuclear plants). It is inappropriate for low-cost autonomous robots because it relies on diagnostic procedures, which require expensive proprioceptive sensors, and contingency plans, which cannot cover all the possible situations that an autonomous robot can encounter. It is here contended that trial-and-error learning algorithms provide an alternate approach that does not require diagnostic, nor pre-defined contingency plans. In this project, we will develop and study a novel family of such learning algorithms that make it possible for autonomous robots to quickly discover compensatory behaviors. We will thus shed a new light on one of the most fundamental questions of robotics: how can a robot be as adaptive as an animal? The techniques developed in this project will substantially increase the lifespan of robots without increasing their cost and open new research avenues for adaptive machines.

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