Post-Doctoral Research Visit F/M Identification robotique des paramètres musculaires

Inria is a national research institute dedicated to digital sciences that promotes scientific excellence and transfer. Inria employs 2,400 collaborators organised in research project teams, usually in collaboration with its academic partners.
This agility allows its scientists, from the best universities in the world, to meet the challenges of computer science and mathematics, either through multidisciplinarity or with industrial partners.
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The researcher will be part of the CAMIN team at INRIA.

The position will be funded by INRIA.

The aim of this project is to develop new methods for muscle parameters identification, using robotics platforms. The ambition is to tackle this still open issue using an innovative experimental setup and dedicated estimation and control algorithms developed in a unified framework.

The development of accurate and personalized NMSK models is essential for changing paradigms of rehabilitation and interactions involving assistive devices. One of the main challenges is to identify these models parameters [1]. Efficient robotics approaches have been developed [2], that do not generalize to human models as they do not address muscular and cellular parameters identification.
In this project, we want to address muscle parameters identification thanks to a unique setup combining the computation of precise parameter-exciting trajectories [3] and the use of robots as position and force sensors, in contact with the patient’s body. Their precise kinematics and embedded force sensors make them perfect experimental platforms to quantify body motions and adapt the model’s parameters based on their measurements. We plan to develop a formulation that will combine, in a single real-time optimization problem, the estimation of the user-specific NMSK parameters and the computationof the future exciting movements of the upper limb, in order to guarantee a good observability of the parameters of interest. Implemented as a combination of a sliding window estimator and a nonlinear model predictive control algorithm [4], the relevance of this approach lies in the exploitation of the fact that the same dynamical system is controlled and estimatedat the same time [5]. This unified formulation will ensure the sensitivity of the variables to be estimated with respect to the control variables

[1] G. Valente, L. Pitto, D. Testi, A. Seth, S. L. Delp, R. Stagni, M. Viceconti, and F. Taddei, “Are subject-specific musculoskeletal models robust to the uncertainties in parameter identification?”PLoS One, vol. 9, no. 11, p. e112625, 2014.

[2] J. Jovic, F. Philipp, A. Escande, K. Ayusawa, E. Yoshida, A. Kheddar, and G. Venture,“Identification of dynamics of humanoids: Systematic exciting motion generation,” in2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).IEEE, 2015, pp. 2173–2179

[3] A. D. Wilson, J. A. Schultz, and T. D. Murphey, “Trajectory optimization for well-conditioned parameter estimation,”IEEE Transactions on Automation Science and Engineering, vol. 12, no. 1,pp. 28–36, 2014

[4] M. Neunert, C. De Crousaz, F. Furrer, M. Kamel, F. Farshidian, R. Siegwart, and J. Buchli,“Fast nonlinear model predictive control for unified trajectory optimization and tracking,” in2016IEEE international conference on robotics and automation (ICRA). IEEE, 2016, pp. 1398–1404.

[5] M. Mukadam, J. Dong, F. Dellaert, and B. Boots, “Steap: simultaneous trajectory estimationand planning,”Autonomous Robots, vol. 43, pp. 415–434, 2019

Collaboration :
The recruited person will be in connection with other INRIA teams for the robotics experiment.

Main activities:

• Literature review on the calibration of muscle models
• Writing of an application to the local ethics committee of the INRIA center to obtain authorization to conduct the experiments required for the project
• Drawing up technical specifications for experimental platforms
• Formalization of a theoretical framework unifying muscle parameter estimation and robot control
• Implementation of experimentation on valid subjects
• Dissemination of results (publications and scientific communications)
  
  

• Subsidized meals
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking (few days per week) and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Contribution to mutual insurance (subject to conditions)

Gross Salary: 2788 € per month