[robotics-worldwide] [jobs]: 3 PhD positions in "Motor Learning and Rehabilitation Robotics" with scholarship at IIT

Jacopo Zenzeri jacopo.zenzeri at iit.it
Mon Apr 30 11:06:57 PDT 2018


3 PhD positions in "Motor Learning and Rehabilitation Robotics" with
scholarship starting from November 2018 at the Robotics, Brain and
Cognitive Sciences Department, Istituto Italiano di Tecnologia,
https://urldefense.proofpoint.com/v2/url?u=http-3A__www.iit.it_rbcs&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=9Tvv5H5fn3mjqaWBZM6TyISF7-m6IuWxQw6V8MargaI&s=MxXfcL2TUWREoGH5s9ZblHX0fgyeVqBErfuXJ-5nDDM&e=

* Official call: PhD Program in Bioengineering and Robotics. Curriculum on
Cognitive Robotics, Interaction and Rehabilitation Technologies:
https://urldefense.proofpoint.com/v2/url?u=http-3A__phd.dibris.unige.it_biorob_index.php_how-2Dto-2Dapply&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=9Tvv5H5fn3mjqaWBZM6TyISF7-m6IuWxQw6V8MargaI&s=z5kkQXR3Wcq_PobQN-ojwCzJ3pvqxNybGUejjX6WZb0&e=
<https://urldefense.proofpoint.com/v2/url?u=http-3A__phd.dibris.unige.it_biorob_index.php_how-2Dto-2Dapply&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=9Tvv5H5fn3mjqaWBZM6TyISF7-m6IuWxQw6V8MargaI&s=z5kkQXR3Wcq_PobQN-ojwCzJ3pvqxNybGUejjX6WZb0&e=>

https://urldefense.proofpoint.com/v2/url?u=https-3A__www.iit.it_phd-2DSchool&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=9Tvv5H5fn3mjqaWBZM6TyISF7-m6IuWxQw6V8MargaI&s=TKBYRJsmQbZni18VQwZqOwzn4Rdb7HRqjZ6ItzLM470&e=
* Application *must* be done online before June 12, 2018.
* Applicants are strongly encouraged to contact: Jacopo Zenzeri <jacopo
.zenzeri_(at)_iit.it>

*Theme 1: Robotic assessment and training to speed up sensorimotor skill
recovery*
*Tutor**:* Dr. Jacopo Zenzeri, Prof. Pietro Morasso
*Research Unit:* Robotics, Brain and Cognitive Sciences (
https://urldefense.proofpoint.com/v2/url?u=https-3A__www.iit.it_rbcs&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=9Tvv5H5fn3mjqaWBZM6TyISF7-m6IuWxQw6V8MargaI&s=WkvdEmeSYN2C2InH7PcxL6BXXYTiOOQgZM4GBMlZgMc&e=)
*Division:* Motor Learning, Assistive and Rehabilitation Robotics
*Description:* A great majority of neurological patients present both
motor dysfunctions
and impairments in kinesthesia, but traditional robot and virtual
reality training
techniques focus either in recovering motor functions or in assessing
proprioceptive deficits. The open challenge is to implement effective and
reliable assessment and training protocols for  sensorimotor recovery that
exploit the interaction capabilities of the robots. In order to do it the
mechanisms underlying sensorimotor deficits have to be studied from a
computational point of view and translated into control algorithms in
rehabilitation robots. The research will involve experiments with human
subjects (children and adults - healthy and impaired) using haptic
interfaces, analysis of movements and their neural correlates (using EMG,
EEG, TMS). The knowledge gained from the experiments will be also used to
design more effective haptic systems to be delivered directly to
clinicians. This will imply a real push of the prototypes improved during
the research period toward products and the concrete transfer of them in
the clinical environment. The clinical experimental activities will be
carried out inside major hospitals where IIT has formalized collaborations.
*Requirements:* a master degree in Bioengineering, Computer Science or
equivalent, with experience in the analysis and modeling of human movements
and in robot programming. Attitude for experimental work, problem solving
and computational modeling will constitute factors of preference.
*References:*
1. De Santis D, Zenzeri J, Casadio M, Masia L, Riva A, Morasso P and Squeri
V (2015) Robot-assisted training of the kinesthetic sense: enhancing
proprioception after stroke. Front. Hum. Neurosci. 8:1037, doi:
10.3389/fnhum.2014.01037
2. Squeri V, Masia L, Giannoni P, Sandini G, Morasso P (2014) Wrist
rehabilitation in chronic stroke patients by means of adaptive, progressive
robot aided therapy. IEEE Trans. Neural Systems and Rehab Engineering,
22(2): 1-14, doi:10.1109/TNSRE.2013.2250521

*Theme 2: Optimizing haptic interaction for vocational training*
*Tutor:* Dr. Jacopo Zenzeri, Prof. Pietro Morasso
*Research Unit:* Robotics, Brain and Cognitive Sciences (
https://urldefense.proofpoint.com/v2/url?u=https-3A__www.iit.it_rbcs&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=9Tvv5H5fn3mjqaWBZM6TyISF7-m6IuWxQw6V8MargaI&s=WkvdEmeSYN2C2InH7PcxL6BXXYTiOOQgZM4GBMlZgMc&e=)
*Division:* Motor Learning, Assistive and Rehabilitation Robotics
*Description:* In the last decade there has been a growing interest in
studying physical coupling between humans or humans and machines. Indeed,
developing a machine capable of understanding the intention of a movement
and interactively cooperate with a human is among the frontiers of the
research in robotics as well as rehabilitation and vocational training. In
the last cited field the focus is on the motor skill transfer from an
expert human agent to a naïve one in a dyadic configuration [1]. The main
idea behind the PhD project is to pass from a dyadic configuration to a
triadic one where two human exchange haptic information mediated by a robot
that play an active role. The objective of the robot is to convey and shape
the haptic information coming from the expert human to the naïve one
maximizing the learning process [2]. The research will involve experiments
with human subjects (healthy and impaired) using haptic interfaces,
mathematical modeling of human control strategies and design of more
effective robots able to effectively transfer skills.
*Requirements:* a master degree in Bioengineering, Computer Science or
equivalent, with experience in the analysis and modeling of human movements
and in robot programming. Attitude for experimental work, problem solving
and computational modeling will constitute factors of preference.
*References:*
1. Avila-Mireles, E.J. et al., 2017. Skill learning and skill transfer
mediated by cooperative haptic interaction. IEEE Transactions on Neural
Systems and Rehabilitation Engineering, 25(7), pp.832–243.
2. Galofaro, E., Morasso, P. & Zenzeri, J., 2017. Improving motor skill
transfer during dyadic robot training through the modulation of the expert
role. In IEEE International Conference on Rehabilitation Robotics. London.

*Theme 4: Transferring of human-robot interaction competencies: towards
robot symbiosis in the acquisition of new skills*
*Tutor:* Jacopo Zenzeri, PhD; Alessandra Sciutti, PhD; Francesco Rea, PhD
*Research Unit:* Robotics, Brain and Cognitive Sciences (
https://urldefense.proofpoint.com/v2/url?u=https-3A__www.iit.it_rbcs&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=9Tvv5H5fn3mjqaWBZM6TyISF7-m6IuWxQw6V8MargaI&s=WkvdEmeSYN2C2InH7PcxL6BXXYTiOOQgZM4GBMlZgMc&e=)
*Division:* Motor Learning, Assistive and Rehabilitation Robotics &
Cognitive Robotics and Human-Human Interaction
*Description:* In the recent years many studies focused on how to optimize
human-robot collaboration tasks. Here we focus on a fundamental case where
a human agent has to  learn how to use a tool while collaborating with an
expert humanoid robot agent which, in this case, plays the active role of
maximizing the learning of the human. In this new  perspective the process
will proceed in two steps: 1) the humanoid robot acquire the knowledge of
the collaborative tasks and becomes the “robot teacher” by interacting
(through the tool) with an expert human; 2) the naïve human interact
(through the tool) with the “robot teacher” in order to learn the task in
an optimal way. Behavioral  experiments on motor learning will be conducted
using haptic interfaces to study motor control mechanisms and how motor
control strategies emerge during the interaction with specific set of
tools. This activity is based on recent studies on dyadic interaction [1-2]
and will contribute to define human inspired models of interaction. The
model is transferred to the humanoid agent to acquire the human expert
knowledge (in step 1) and to teach it to the naïve one (in step 2).
Behavioral experiments will then be conducted with the humanoid robot iCub
to implement real use cases. In this context, the proactive role of the
iCub will enrich existing cognitive framework for human robot interaction.
The  candidate will also exploit measure of engagement in the task
(attentional level, cognitive load and fatigue).
*Requirements:* a master degree in Bioengineering, Computer Science or
equivalent, with experience in the analysis and modeling of human movements
and in robot programming.
*References:*
1. Avila-Mireles, E.J. et al., 2017. Skill learning and skill transfer
mediated by cooperative haptic interaction. IEEE Transactions on Neural
Systems and Rehabilitation  Engineering, 25(7), pp.832–243.
2. Galofaro, E., Morasso, P. & Zenzeri, J., 2017. Improving motor skill
transfer during dyadic robot training through the modulation of the expert
role. In IEEE International  Conference on Rehabilitation Robotics. London.
3. Vignolo A., Noceti N., Rea F., Sciutti A., Odone F. & Sandini G. 2017,
‘Detecting biological motion for human-robot interaction: a link between
perception and action’,  Frontiers in Robotics and AI, 4.

Jacopo Zenzeri, PhD
Head of Motor Learning, Assistive and Rehabilitation Robotics Lab
Robotics, Brain and Cognitive Sciences Department
Center for Human Technologies
Istituto Italiano di Tecnologia, Genova, Italy


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