PostDoc in Assistive robotics and motor neuroprostheses for mobility: shared control for wheelchair driving and reach-to-grasp assistance

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PostDoc Position in

Assistive robotics and motor neuroprostheses for mobility: shared control for wheelchair driving and reach-to-grasp assistance

Team: Rainbow IRISA/Inria Rennes

Advisors: Marie Babel and Guillaume Charvet 

Scientific contact: marie.babel@irisa.fr –  guillaume.charvet@cea.fr

Project site: team.inria.fr/rainbow

How to apply:  See below

 

Context

People with disabilities face various individual situations and conditions, while sharing the same challenge: being as independent as possible for their everyday activities, despite any partial or total function loss, e.g. a reduced mobility. Assistive technologies are then designed to compensate for motor deficiencies and have to be adapted to a large spectrum of pathologies.


However, assistive technologies imply that the user remains able to initiate the mobility task, and that his/her remaining capacities can be sufficient to perform the given task in a secure and acceptable manner. In this context, the INTERCARNOT MOVE@HOME project between LETI/CLINATEC (Grenoble) and INRIA/RAINBOW (Rennes) aims to allow tetraplegic patients to benefit from a motor neuroprosthesis in order to provide a safe, robust and precise control of an assistive device for daily life activities, such as power wheelchair driving, and the control of a robotic exoskeleton arm.

The objective of the project is to tackle this challenge by developing software and hardware solutions for shared control between semi-invasive Brain Computer Interface (BCI) and robotic assistance for reach-to-grasp task and power wheelchair navigation.

This position will necessitate regular travels between Inria in Rennes (main location) and LETI/Clinatec in Grenoble. Travel expenses are covered within the limits of the scale in force.

 

PostDoc Subject

The goal of the post-doctoral work will be to design the shared control framework that will rely on sensor-based servoing strategies. Then, by combining the LETI/CLINATEC neuroprosthesis and the proposed shared control strategy, we will propose first a demonstration of this technology on a safe wheelchair driving application. Shared control will make it possible to leave “natural” control of the wheelchair to the patient (by cerebral control), while ensuring safe trajectories by avoiding obstacles. In a second step, a solution for secure and precise control of the reaching and grasping of objects will be proposed.

Within the Inria/Rainbow team, the researcher will have to

  • Validate an adapted shared control strategy for power wheelchair driving,
  • Design a new shared control framework for reach-to-grasp tasks by means of innovative sensors such as a novel generation of embedded sensors for 3D or proximity sensing,
  • Perform user studies.


The PostDoc research will naturally fit into our several activities over the last years about the general problems of shared control of robots and control/estimation for multi-robot systems. A selection of relevant works can be found at the end of the page (References), and here some videos showing previous results on related topics: video1, video2, video3, video4, video5, video6, video7.

The facilities in the team include a vicon-instrumented indoor room for experiments with multiple quadrotors, four manipulator arms (two 6-dof industrial manipulators, and two 7-dof torque-controlled manipulators), and state-of-the-art 6-dof haptic devices. Exploiation of the Immersia VR facility (on campus) for running human/multi-robot experiments will also be possible (and encouraged).
Finally, two research engineers (technical staff) will assist the PostDoc for all what concerns hardware/software development and maintenance of the robotic platforms.

 

Skills/Requirements

Technical skills:

  • Interdisciplinary skills including robotics, computer vision;
  • C/C++ programming;
  • Familiarity with ROS;
  • System integration, Electronics and Mecatronics would be a plus;

Relational skills :

  • Capacity to efficiently work in a scientific environment, passion for experimental research
  • Capacity to conduct independent work within a team
  • The applicant should be comfortable conducting clinical studies with people with disabilities.

Conditions

The position is full-time for 1 year. The position will be paid according to the French salary regulations for Postdoctoral scholars depending on the candidate’s previous experience

 

How to apply

Candidates can apply throught this form or should provide a cover letter, a CV and a list of references via email to

The position will remain open until a satisfactory candidate is found

 

References

[1] Scholtz N., “Assistive technologies to support people with disabilities”. Europ. Parliament Research Serv., 2015.

[2] E. Leblong, B. Fraudet, L. Devigne, M. Babel, F. Pasteau, N. Benoit, P. Galien. SWADAPT1 : Evaluation on standardised circuits of the interest of a robotic module for assisting the driver of an electric wheelchair: pilot, prospective, controlled, randomised study. Annual Meeting and International Society of Physical & Rehabilitation Medicine World Congress, ISPRM 2020, Mars 2020.

[3] Gupta A., et al. “Developments and clinical evaluations of robotic exoskeleton technology for human upper-limb rehabilitation.” Advanced Robot., 34(15), 1023-40 (2020).

[5] Lee S. H., et al., “Comparisons between end-effector and exoskeleton rehabilitation robots regarding upper extremity function among chronic stroke patients with moderate-to-severe upper limb impairment”. Scientific Reports, 10, (2020).

[6] Jain S., et al., “Grasp Detection for Assistive Robotic Manipulation”. IEEE ICRA’16, 2015-2021 (2016).

[7] Gull M. A., et al.,  “A review on design of upper limb exoskeletons”. Robotics,  9(1), (2020).

[8] Barsotti M., et al., “A full upper limb robotic exoskeleton for reaching and grasping rehabilitation triggered by MI-BCI”, IEEE ICORR’15, 49-54 (2015).

[9] Cunningham P., et al. “Task-specific reach-to-grasp training after stroke: development and description of a home-based intervention”. Clinical Rehabilitation, 30(8), 731-740 (2016).

[10] Nguiadem C., et al., “Motion Planning of Upper-Limb Exoskeleton Robots: A Review”. Applied Sciences, 10(21), 7626 (2020).

[11] Mohebbi A., “Human-Robot Interaction in Rehabilitation and Assistance: a Review”. Currents Robotics Report, 1, 131–144 (2020).

[12] Jain S., et al., “Assistive Robotic Manipulation through Shared Autonomy and a Body-Machine Interface”. IEEE ICORR’15, (2015).

[13] Devigne L., et al., ”A shared control solution for safe assisted power wheelchair navigation in an environment consisting of negative obstacles: a proof of concept”. IEEE SMC’19, (2019).

[14] Devigne L., et al., “Power wheelchair navigation assistance using wearable vibrotactile haptics”. IEEE Trans. on Haptics, 13(1), 52-58 (2020).

[15] Mestais C., et al., “WIMAGINE: Wireless 64-Channel ECoG Recording Implant for Long Term Clinical Applications”. IEEE Trans. on Neural Systems and Rehab. Eng. 23(1), 10-21 (2015).

[16] Benabid A. L., et al. “Chronic Epidural Wireless Brain Machine interface drives an exoskeleton and restores four-limb mobility in a tetraplegic patient”. Lancet Neurology, 18(12), 1112-1122 (2019).

[17] Pedersen O.-M., et al., “Grasping Unknown Objects by Coupling Deep Reinforcement Learning, Generative Adversarial Networks, and Visual Servoing”. IEEE Int. Conf. on Robotics and Automation (ICRA), 5655-5662 (2020).

 

 

 

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