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PdD : Identification of multivariable models for human-robot co-manipulation with passivity certificates

French version here

This PhD is part of a joint collaboration between ICube (Edouard Laroche), LIAS (Guillaume Mercère) and CEA (Neil Abroug). The location is at CEA-Saclay.

  • Context

Co-manipulation systems, also called cobots, are designed to interact with human operators physically in order to assist them in their work tasks. For instance, they provide force enhancement for handling or cutting tasks. Like any usual robotic systems, cobots are equipped with actuators and position sensors. Usually, they are also equipped with force sensors.

These robotic systems interact with uncertain and time-varying environments: on the one hand, with the operator who grips the handle more or less rigidly and, on the other hand, on the effector side, depending on the task it has to perform, either lifting a load of unknown mass or cutting an inhomogeneous material, to mention only simple cases. These environments are generally assumed to be passive: they only dissipate the energy provided to them. This property of passivity of the environment makes it possible to ensure the stability of the cobot in interaction with its environment, provided that it is itself passive.

Different approaches are available to identify a model of a robot. The so-called gray box methods rely on a model structure provided by the laws of physics, the parameters of which must be estimated. They result in passive models that are relatively compact and parsimonious, but have the disadvantage of not being able to easily encompass dynamics that are difficult to model physically but that can be crucial for the performance of the control. Black-box techniques (in the frequency domain with continuous-time models) are more suitable for obtaining powerful models for controller design in particular in the presence of flexible modes. However, the available methods are not able to ensure that the provided model is passive with satisfying accuracy.

  • Issue

In this thesis, methods will be developed for the identification of multivariable models from frequency data, that suit to the context of co-manipulation and with the goal of enabling the synthesis of robust control laws. The aim is to nicely combine the advantages of black-box and gray-box models to lead to passive representations capable of representing complex dynamics such as high frequency flexible modes. Experimental validation will be carried out on the demonstrators available at CEA-LIST.

  • Profile of applicant

This PhD proposal mainly requires skills in applied mathematics, automatic control and system identification more specifically. Fluency in French or in English is required. Thus, we are looking for candidates graduated from a Master of Science or an Engineering school with a solid scientific background with a specialization in control system theory and, of possible, in robotics. Your curiosity, your ability to analyze, your communication skills, as well as your commitment, will be a guarantee of success for the project.

  • Application

Please, provide:

- Curriculum Vitae and cover letter,

- Master marks,

- Certificate of proficiency in English,

A letter of recommendation will be appreciated. Any other document deemed necessary by the candidate which can enrich the application. Please send these documents to, and before April 27, 2018. In your cover letter, you must show your understanding of the subject and highlight your skills related to the topic. All documents will be provided in the form of a single pdf document whose title will be: NAME-Firstname-thesis-identifier-cobot.pdf.

Two PhD Positions in Computer Vision / Deep Learning within project CAMMA

The operating room is a high-tech environment in which the equipment generates a lot of data about the underlying surgical activities. Our research group aims at making use of this large amount of multi-modal data coming from both cameras and surgical devices to develop an artificial intelligence system that can assist the clinicians and staff in the surgical workflow. In this scope, we currently have two open PhD positions that will focus on developing new machine learning and computer vision methods to detect, recognize and analyze human activities. The successful candidates will have the rare opportunity to apply their work on large RGB-D and endoscopic video datasets captured during real procedures using the state-of-the-art facilities of our clinical partners. If interested, they will also have the exceptional possibility to collaborate with engineers and clinicians to implement real-time clinical demonstrators of their research, thereby contributing to the development of real-world AI-based solutions for the OR.
More information is available here.

Open Postdoc or Research Engineer Position in Computer Vision within Project CAMMA

We are looking for an engineer in Computer Vision to join the development of our clinical prototypes aiming at monitoring safety during image-guided interventions. The project involves the perception of the Operating Room through a set of RGB-D cameras mounted on the ceiling as well as the recognition of surgical activities using both RGB-D and endoscopic videos. More information is available here.

Open Internship Positions within Project CAMMA

We are looking for motivated and talented students with knowledge in computer vision, machine learning and/or augmented reality who can contribute to the development of our computer vision system for the operating room.

Please feel free to contact Nicolas Padoy if you are interested to do your master's thesis or an internship with us (funding of ~500Euros/month will be provided during 3 to 6 months). The successful candidates will be part of a dynamic research group hosted within the IRCAD institute at the University Hospital of Strasbourg. They will thereby have direct contact with clinicians, industrial partners and also have access to an exceptional research environment. The CAMMA project is supported by the laboratory of excellence CAMI, the IdEx Unistra and the MixSurg Institute.


  • Deep Learning for the Analysis of Large Surgical Video Databases
  • Multi-view Human Body Tracking for the Operating Room
  • 3D Simulation and Visualization of X-ray Radiations for Radiation Safety Analysis

More information about CAMMA


Computer vision and medical images processing

  • Title: Robust tracking of moving tissues in colonoscopy videos
  • Training period: 5-6 monthes, between January and August 2018
  • Supervisor: F. Nageotte (
  • Keywords: robust tracking, features detection, surgical endoscopy
  • Context:

This work takes place in the development of tools for assisting gastroenterologists and surgeons in manipulating flexible endoscopes for medical tasks in the digestive tract. One of the goals is to automatically and actively track areas of tissues with robotic endoscopes during physiological motion. For this purpose, areas of tissues have to be robustly tracked in the endoscopic images.


Many solutions have been proposed in the past few years to track tissues in endoscopic images [M. Yip 2012, V.Penza 2017]. However, most of these techniques have been tailored for laparoscopic surgery. Images provided by colonoscopes (see figure) differ notably from laparoscopic images for the following reasons:

    • The environment is uniform, with only the mucosa of the digestive tract visible,
    • The orientation of tissues with respect to the camera is more varied, with tissues on the side (like in a pipe),
    • Cameras embedded in conventional flexible endoscopes provide images with lower resolution and with lower quality than in laparoscopes,
    • Physiological motion such as peristaltic waves create very strong tissue deformations.
  • Work of the intern:

The objective of the internship will be to identify the tracking techniques which are adapted to these particular conditions. Real images acquired during surgery in the colon of pigs will be used as support. The work will therefore consist in: - Implementing and testing existing algorithms on colonoscopy sequences - Identifying limitations of these algorithms - Adapting and improving them to fill the requirements of robust tracking. The implementation will be made in Matlab and / or C++/ open CV depending on the availability of existing codes. The work will be carried out on the robotic platform of the AVR team of the ICube laboratory, located at the hospital in the center of Strasbourg.

  • Profile of candidates : Master student (or student from engineering school) with major in computer vision or real-time image processing. Interest for / knowledge in medical robotics application can be a plus but is not a requirement. Proficiency in Matlab and C/C++ are required. Knowledge of OpenCV will be appreciated.
  • To apply: send a CV, cover letter, available grades at master level and programs of master courses to Florent Nageotte :

Medical robotics and electromagnetic tracking

  • Title: Electromagnetic tracking of continuum robots
  • Training period: 5-6 monthes, between January and August 2018
  • Supervisor: F. Nageotte ( and P. Cantillon-Murphy
  • Keywords: Continuum robots, endoscopy, EM tracking

The complete proposal in pdf is available here.

  • Context:

In this work, one aims at investigating the use of electromagnetic (EM) sensing for measuring and controlling flexible endoscopic systems. EM tracking is an attractive technique for providing external sensing on continuum systems because no line of sight is needed and sensors have very small sizes. For this purpose, the ICube laboratory is collaborating with the team of Padraig Cantillon-Murphy at University College Cork, Ireland. This team has developed an open EM tracking system called Anser [2].

The objective of the work will consist in developing systems, techniques and software for an efficient tracking of endoscopes and endoscopic instruments.


  • Work of the intern:

The work will consist in three main stages:

    • First the working of the ANSER EM will have to be understood and the tracking of flexible instruments will be analyzed to serve as a baseline for subsequent developments. This will be done in static as well as dynamic conditions (movements).
    • The origin of accuracy limitations, for instance the presence of metallic parts and motorization, or delays in acquisition will be identified
    • Improvement on the setup, on different aspects: mechatronics (attachment of sensors), electronics (adaptation of EM frequencies), software (filtering, etc.).

Testing will be performed with the ANSER EM tracking system both on manual flexible instruments and on the STRAS robotic system of the ICube laboratory [3] (see figure).

  • Profile of candidates : Student at the master level with major in electrical engineering or mechatronics with strong experimental skills (electronics, mechatronics, software development).

Experience in electromagnetism would be a plus.

  • To apply: send a CV, cover letter, available grades at master level and programs of master courses to Florent Nageotte :