Pascal Brault

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Pascal Brault

Ph.D. student, Inria, Université de Rennes 1

Email : pascal.brault@irisa.fr
Address :
IRISA, INRIA Rennes – Bretagne Atlantique
Campus Universitaire de Beaulieu
35042 Rennes, France
Assistant : 02 99 84 22 52 (Hélène de La Ruée)

 

 

Short bio

I studied mechatronics at the ENS de Rennes from 2015 to 2019, thus graduating mechanics and electronics degrees. In 2018, I obtained the french « agrégation » opening for teaching positions, in engineering industrial sciences, with mechanical engineering option.

I have been a part of the Rainbow team as Ph.D. from september 2019, under the supervision of Paolo Robuffo Giordano and Quentin Delamare, on the thematic of trajectory generation for mobile robotics, with parametric uncertainty considerations.

I also teach at the ENS de Rennes in mechatronics-related fields such as mechanical systems modeling, and in the preparation for the writings/orals of agrégation, in the mechanical engineering option.

 

ADAPT Project – Innovation award at SOFMER 2019

The Interreg ADAPT project (http://adapt-project.com/) aims to define innovative mobility solutions for people with disabilities. A power wheelchair simulator has been co-created with the medical staff of the Pôle Saint Hélier, a rehabilitation center in Rennes. Our joint work was rewarded by LNA Santé during the SOFMER Congress 2019.

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Samuel Felton

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Samuel Felton

Phd student, Université Rennes 1

Email : samuel.felton @ irisa.fr
Address :
IRISA, INRIA Rennes – Bretagne Atlantique
Campus Universitaire de Beaulieu
35042 Rennes, France
Assistant : 02 99 84 22 52 (Hélène de La Ruée)

 

 

Bio

I received a double master’s degree (computer science engineering – research-oriented computer science) from the INSA Rennes engineering school in 2019. My last master’s year has been heavily focused on the areas of computer vision and machine learning. Beforehand, I studied at the IUT du Limousin and received a « DUT informatique », a technical computer science degree.

I have completed two internships related to machine learning and vision problems.

  • The first one was done at Robovision in 2018 and aimed at improving the performance of a neural network trained to regress the orientation of tulip bulbs that were to be prepared for plantation by a robotic arm.  I worked with both point clouds and raw images of the bulbs to improve orientation regression.
  • The second one took place at Inria in 2019 and concluded my master’s degree.  The subject of this internship was to apply deep learning to the problem of Visual servoing. It is the foundation on which my PhD started.

The topic of my PhD is the following: « Deep learning for visual servoing » and is under the supervison of Elisa Fromont (LACODAM team) and Eric Marchand (Rainbow team). I will be exploring how to apply deep learning techniques to perform efficient visual servoing. As of now, I am focusing on performing visual servoing in an end-to-end fashion and regressing the camera velocity directly instead of estimating the camera poses (that can be used in Position-Based Visual Servoing).