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Master Internship in Deformation Modelling for Robot Manipulation

24 Novembre 2022


Catégorie : Stagiaire


The Imagine team at LIRIS laboratory, Lyon- France is offering a master internship in geometric modeling of rigid and deformable object for robotic manipulation .

The project will be supervised by Prof. Liming Chen (liming.chen@ec-lyon.fr) and Dr. Shaifali Parashar (shaifali.parashar@liris.cnrs.fr) .

Requirements:
Masters in computer vision, robotics, machine learning, mathematics or any field related to the topic
Strong programming skills in C++ and python
Fluency in English
Project duration: 6 months
Tentative start date: February 2023
How to apply:
Please send your CV, transcripts and 2 reference letters to Liming Chen and Shaifali Parashar with the subject "Internship:Deformable Object Modelling ".

 

Robotic manipulation is a highly sought-after problem. With the success of industrial robots for manipulating objects with a ‘pick and place’ approach, there is a lot of research aiming at its extension to generic, real-life objects. The industrial robots deal with a limited set of objects in a highly-controlled environment to accomplish the ‘pick and place’ task. Contrastingly, in real-life, environments are rather complicated and the robots may have to deal with a wide variety of objects. These objects may vary immensely in terms of their size, geometry, texture and deformability.

The major challenge for a robot involved in object manipulation is to understand how an object deforms when it is subjected to an external force. So far, most manipulation applications focus on rigid objects, which do not require deformation modelling. However, to manipulate deformable objects, it is absolutely essential to model their deformations, which is the goal of this project.

Based on the deformations, the objects can be categorised as rigid or deformable, elastic or inelastic, volumetric or thin-shell. In our previous works[1], we have shown that deformations can be modelled with a high accuracy with local geometric properties of the objects under consideration. Such a modelling has been shown to be fast, accurate and therefore, effective for the 3D reconstruction of various deformable objects, including elastic[2] and volumetric objects[3], from monocular images.

In this project we will extend the use of local geometric properties to the robotic context. We consider some of the common real-life objects, available in YCB dataset[4]. Given a robot which is equipped with multiple imaging and depth sensors, we will use the local geometric properties of deformation to predict the robot-object interaction.

 

[1] Parashar et al, TPAMI 2017. Isometric Non-Rigid Shape-from-Motion with Riemannian Geometry in Linear Time.

[2] Parashar et al, CVPR 2020. Local Non-Rigid Structure-from-Motion from Diffeomorphic Mappings.

[3] Parashar et al, ICCV 2015. As-Rigid-As-Possible Volumetric Shape-From-Template.

[4] https://www.ycbbenchmarks.com/