Scientific direction Development of key enabling technologies
Transfer of knowledge to industry

PostDocs : selection by topics

Technological challenges >> Factory of the future incl. robotics and non destructive testing
4 proposition(s).

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Numerical Meta-modelization based study of the propagation of ultrasonic waves in piping system with corroded area

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Simulation et Modélisation en Acoustique

01-05-2020

PsD-DRT-20-0055

vahan.baronian@cea.fr

The aim of the ANR project PYRAMID (http://www.agence-nationale-recherche.fr/Projet-ANR-17-CE08-0046) is to develop some technics of detection and quantification of the wall thinning due to flow accelerated corrosion in piping system. In the framework of this project involving French and Japanese laboratories, CEA LIST develops new numerical tools based on finite elements dedicated to the modelling of an ultrasonic guided wave diffracted by the corrosion in an elbow pipe. These solutions support the design of an inspection process based on electromagnetic-acoustic transduction (EMAT). To this end, the ability of CEA LIST to adapt meta-modeling tools of its physical models will be the key asset to allow intensive use of the simulation.

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High precision robotic manipulation with reinforcement learning and Sim2Real

Département Intelligence Ambiante et Systèmes Interactifs (LIST)

Laboratoire Vision et Apprentissage pour l'analyse de scènes

01-09-2020

PsD-DRT-20-0082

jaonary.rabarisoa@cea.fr

High precision robotic assembly that handles high product variability is a key part of an agile and a flexible manufacturing automation system. To date however, most of the existing systems are difficult to scale with product variability since they need precise models of the environment dynamics in order to be efficient. This information is not always easy to get. Reinforcement learning based methods can be of interest in this situation. They do not rely on the environment dynamics and only need sample data from the system to learn a new manipulation skill. The main caveat is the efficiency of the data generation process. In this post-doc, we propose to investigate the use of reinforcement learning based algorithms to solve high precision robotic assembly tasks. To handle the problem of sample generation we leverage the use of simulators and adopt a sim2real approach. The goal is to build a system than can solve tasks such as those proposed in the World Robot Challenge and tasks that the CEA's industrial partners will provide.

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Fibre optic sensor instrumentation for thermomechanical measurements in harsh environment

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Capteurs Fibres Optiques

01-07-2021

PsD-DRT-21-0085

cyril.lefeuve@cea.fr

In connection with the people in charge of the project in the LCFO laboratory (DRT Saclay) and the LMES laboratory (DAM Cesta), the postdoctoral fellow will participate in the development of the on-board interrogation system, from both a system and an algorithmic point of view, as well as in the development of the fibre optic sensor test on the SPRITE installation. The person recruited will also be in contact with the LRP laboratory (DAM Le Ripault) in order to participate in tests of fibre integration within plasma-sprayed ceramics. He or she will carry out measurements of the spectral quality of the integrated sensor and will analyse these spectra once the integration has been carried out but also during the integration. The person recruited will be based mainly on the Saclay site but will be required to travel for a few days to the CEA Cesta and CEA Le Ripault sites in order to set up and carry out the experiments. Skills in optics, instrumentation, embedded systems and algorithms are required.

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Elaboration of a common robot/human action space

DPLOIRE (CTReg)

Autre DPLOIRE

01-06-2021

PsD-DRT-21-0092

laurent.dolle@cea.fr

This post-doc aims at establishing by artificial intelligence methods (e.g. signal processing on graphs), the mapping of an industrial task performed by a human operator, and acquired by visual sensors, in order to be interpretable and exploitable by a robot. It is part of a project aiming at designing a demonstrator in which a robot will learn to reproduce by observation a task performed by a human. The platform has been deployed at CEA Tech and is currently operated by an engineer. The objective of this post-doc is mainly to study and develop a set of methods to build a mapping between the actions performed by a human operator and perceived through visual sensors and the actions performed by the robot. These methods and the work of the related theses will then be implemented in the demonstrator in order to test them experimentally. Due to the central position of the subject of this post-doc, under the triple supervision of the PACCE and IPI teams of LS2N and CEA, you will have to collaborate closely with the two PhD students already involved in the project. You will have to conceptualize and formalize the methods and representations on the one hand by synthesizing the existing literature on the subject and on the other hand by establishing a common framework encompassing the two thesis works.

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