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
3 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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Design and control of a multimodal multi-fingered gripper for force and vision-guided robotic manipulation

Département Intelligence Ambiante et Systèmes Interactifs (LIST)

Laboratoire d'Architecture des Systèmes Robotiques

01-01-2021

PsD-DRT-20-0096

mathieu.grossard@cea.fr

The current offer focuses on the design and control approaches of functionally integrated multi-fingered grippers to be used for force- and vision-guided robotic manipulation tasks. The to-be-designed grasping tool will take advantage of multimodal sensing capabilities (embedding tactile sensors, mounted in a meaningful way within the mechatronic device, together with a self-sensing actuation apparatus) combined with a vision system to achieve autonomously fine force-controlled manipulation tasks (such as dexterous in-hand manipulation, pick-and place of rigid and flexible objects, component assembly, etc.). To do so, the previously designed multimodal system will be exploited for learning-by-demonstration tasks (relying on Human/Robot co-manipulation phase) to build a learnt database combining tactile, force and vision cues for a series of objects with various characteristics (in terms of geometry, shape, texture, mass, etc.). This database would first cover known objects, before being extended to objects in various poses and then to unknown objects using learning-based strategies.

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