Estimation of Model Parameters From Observable Data for Augmented Reality and Robotic Control


We are looking for a highly motivated student to work on data assimilation and model parametrization for needle insertion applications.

The position will take place in Strasbourg within the MIMESIS team from Inria, the French National Institute for Computer Science and Applied Mathematics.

One of the main research interests of the MIMESIS team is to propose numerical methods and dedicated physics-based models (such as a model of flexible needle based on Euler-Bernoulli theory) to meet the real-time requirement necessary for intra-operative scenarios. Although the models are validated experimentally, they typically rely on parameters that are not known in a patient specific scenario. The MIMESIS team has been investigating numerical approaches to estimate and refine model parameters from image data acquired during the simulation. Similar approaches have been employed in augmented reality applications in order to provide visual assistance to surgeons but also in robotic systems to provide gesture assistance. Current solutions proposed by the team focus on the estimation of stiffness parameters that mainly impact the deformation of tissues. Nonetheless, there are other parameters significantly influencing the accuracy of the simulation of the needle insertion (e.g., friction and fracture threshold) that cannot be directly extracted from the image data.


In this project, we propose to develop new solutions to increase the accuracy of robotic control employing physics-based simulation. We focus on a scenario, where a robot inserts a flexible needle into a material mimicking soft tissues. While the insertion is captured by a camera system, a sensor attached to the base of the needle provides additional information about the force applied to the needle. The goal of the project is to employ existing state-of-the-art data assimilation methods which would estimate the unknown simulation parameters by fusing the available image and force data, thus improving the outcomes of the simulation controlling the robot.   

Qualifications for applicants

  • Master of science in applied mathematics and/or computer science is required.
  • Good to excellent expertise in C++.
  • Special interests for and competence within medical technology, medical imaging,
  • Development of technology for improved image guided patient diagnostics and therapy.
  • Practical experience with research methods, and R&D.
  • Collaborative skills, initiative, ability to accomplish tasks.
  • Practical skills, and ability to create and establish new projects in collaboration with colleagues
  • Knowledge of error estimation and real-time simulations is a plus.



photo of Hadrien Courtecuisse

Hadrien Courtecuisse

Research Scientist
photo of Stephane Cotin, team leader

Stéphane Cotin

Research Director