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BAYES-RL

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Deep Bayesian Reinforcement Learning -- Unifying Perception, Planning, and Contr...

For robots to assist humanity in homes, or hospitals, the capability to manipulate diverse objects is imperative. So far, however, robotic manipulation technology has struggled in managing the uncertainty and unstructuredness that... ver más

31/03/2027

TECHNION ISRAEL I...

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

TECHNION ISRAEL INSTITUTE OF TECHNOLOGY 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 2022-03-08

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

ERC-2021-STG: ERC STARTING GRANTS

I+D

Cerrada hace 3 años

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1 Participantes

TECHNION ISRAEL INSTITUTE O...

1.50M€ | Lider

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Últimas noticias

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

29-11-2024: CMVAMADRID En las últimas 48 horas el Organismo CMVAMADRID ha otorgado 37 concesiones

Duración del proyecto: 60 meses Fecha Inicio: 2022-03-08
Fecha Fin: 2027-03-31

Líder del proyecto

TECHNION ISRAEL INSTITUTE OF TECHNOLOGY

TRL 4-5

Presupuesto del proyecto 2M€

Descripción del proyecto For robots to assist humanity in homes, or hospitals, the capability to manipulate diverse objects is imperative. So far, however, robotic manipulation technology has struggled in managing the uncertainty and unstructuredness that characterize human environments. Machine learning is a natural approach -- the robot can adapt to a given scenario, even if it was not programmed to handle it beforehand. Indeed, Deep Reinforcement Learning (deep RL), which has recently led to AI breakthroughs in computer games, has been publicized as the learning-based approach to robotics. To date, however, deep RL studies focused on known and observable systems, where uncertainty was resolved by lengthy trial and error. Quickly learning to act in novel environments, as required for robotics, is not yet within our reach. The crux of the matter is the tight coupling between perception and control under high uncertainty -- the robot must actively reduce uncertainty while also trying to solve the task; for complex and high-dimensional systems, we do not have a suitable algorithmic framework for this. In this proposal, our overarching goal is to: Develop the algorithmic framework of using deep learning in problems that tightly couple perception, planning, and control, for advancing robotic AI to reliably manipulate general objects in unstructured environments. Towards this end, we shall develop neural network representations of uncertainty, and algorithms that estimate uncertainty from data. We will develop theory and algorithms for decision making under uncertainty, bringing in a fresh perspective to the problem based on Bayesian reinforcement learning (Bayes-RL). These advances will allow us to study safety certificates for deep RL, and develop a general and practical methodology for learning-based robotic manipulation under uncertainty, validated on real robot experiments. Aside from robotic manipulation, we expect impact on various fields where decision making plays an important role.

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