[robotics-worldwide] [jobs] PhD positions, Machine Learning and Robotics @IIT Genova

Raffaello Camoriano raffaello.camoriano at iit.it
Tue Apr 19 15:42:15 PDT 2016


Machine Learning and Robotics PhD positions (with scholarships) are
available at the iCub Facility and the Laboratory for Computational and
Statistical Learning (LCSL, IIT at MIT), Istituto Italiano di Tecnologia
(IIT). See list of topics below.
Check the official https://urldefense.proofpoint.com/v2/url?u=https-3A__www.iit.it_phd-2Dschool&d=CwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=o8hWZvpMaVyBNU6m87vr1I2HV0EW0SpR1Atec92ZeAE&s=LvoQC6Sq5aGy55sCEcyXVkRT9BWdERqiSxiEYjbYlbs&e= 
<https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.iit.it_owa_redir.aspx-3FREF-3D-2DmzrZjqA2NELodUojuNzbk7513ujBQMFbbucQON77zDJ4sjpoWjTCAFodHRwczovL3d3dy5paXQuaXQvcGhkLXNjaG9vbA&d=CwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=o8hWZvpMaVyBNU6m87vr1I2HV0EW0SpR1Atec92ZeAE&s=zD_Vzv5nZ5oZlotJlye22V8minCZuyF8zWx8I4EyiRA&e= ..>.
and the tips+tricks section for the detailed instructions on how to apply.

Applications must be filed through the University of Genoa using the online
service at this link:
https://urldefense.proofpoint.com/v2/url?u=https-3A__www.studenti.unige.it_postlaurea_dottorati_XXXII_ENG_&d=CwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=o8hWZvpMaVyBNU6m87vr1I2HV0EW0SpR1Atec92ZeAE&s=UIkvzjjQ2-b5VIvFM8xU9bEpwN64c3E6BE7qZZ51NeY&e= 
<https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.iit.it_owa_redir.aspx-3FREF-3DhqDc0qIxV0RCACdKkiLLJNOHcPVA2Gs461uJOnEdTJHJ4sjpoWjTCAFodHRwczovL3d3dy5zdHVkZW50aS51bmlnZS5pdC9wb3N0bGF1cmVhL2RvdHRvcmF0aS9YWFhJSS9FTkcv&d=CwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=o8hWZvpMaVyBNU6m87vr1I2HV0EW0SpR1Atec92ZeAE&s=KDF_bHd5MIRsPXEn3yLV14Ak23vs2a5hkLARXtbXcOc&e= >.
Application
deadline: June 10, 2016 at 12.00 noon (Italian time).
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2) Scene analysis using deep-learning

Tutor: Lorenzo Natale, Lorenzo Rosasco

Description: machine learning, and in particular deep learning methods,
have been
applied with remarkable success to solve visual problems like pedestrian
detection,
object retrieval, recognition and segmentation. One of the difficulties
with these
techniques is that training requires a large amount of data and it is not
straightforward to adopt them when training samples are acquired online and
autonomously by a robot. One solution is to adopt pre-trained convolutional
neural
networks (DCNN) for image representation and use simpler classifiers,
either in
batch or incrementally. Following this approach DCNNs have been integrated
in the
iCub visual system leading to a remarkable increase of object recognition
performance. However, scene analysis in realistic settings is still
challenging due to
scale, light variability and clutter. The goal of this project is to
further investigate and
improve the iCub recognition and visual segmentation capabilities. To this
aim we
will investigate techniques for pixel-base semantic segmentation using
DCNNs and
object detection mixing top-down and bottom-up cues for image segmentation.

Requirements: This PhD project will be carried out within the Humanoid
Sensing and
Perception lab (iCub Facility) and Laboratory for Computational and
Statistical
Learning. The ideal candidate should have a degree in Computer Science or
Engineering (or equivalent) and background in Machine Learning, Robotics and
possibly in Computer Vision. He should also be highly motivated to work on
a robotic
platform and have strong computer programming skills.

References:

Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean
Ma,
Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander
C.
Berg and Li Fei-Fei, ImageNet Large Scale Visual Recognition Challenge,
arXiv:1409.0575, 2014

Jon Long, Evan Shelhamer, Trevor Darrell, Fully Convolutional Networks for
Semantic
Segmentation, CVPR 2015

Pasquale, G., Ciliberto, C., Odone, F., Rosasco, L., and Natale, L.,
Teaching iCub to
recognize objects using deep Convolutional Neural Networks, in Proc. 4th
Workshop
on Machine Learning for Interactive Systems, 2015



3) Implicit learning

Tutor: Lorenzo Natale, Lorenzo Rosasco

Description: machine learning, and in particular deep learning methods,
have been
applied with remarkable success to solve visual problems like pedestrian
detection,
object retrieval, recognition and segmentation. One of the difficulties
with these
techniques it that training requires a large amount of labelled data and it
is not
straightforward to adopt them when training samples are acquired online and
autonomously by the robot. Critical issues are how to obtain large amount of
training samples, how to perform object segmentation and labelling. They
key idea is
develop weakly supervised frameworks, where learning can exploit forms of
implicit
labelling. In previous work we have proposed to exploit coherence between
perceived motion and the robot own-motion to autonomously learn a visual
detector of the hand. The goal of this project is to investigate algorithms
for learning
to recognize object by exploiting implicit supervision, focusing in
particular on the
strategies that allow the robot to extract training samples autonomously,
starting
from motion and disparity cues.

Requirements: This PhD project will be carried out within the Humanoid
Sensing and
Perception lab (iCub Facility) and Laboratory for Computational and
Statistical
Learning. The ideal candidate should have a degree in Computer Science or
Engineering (or equivalent) and background in Machine Learning, Robotics and
possibly in Computer Vision. He should also be highly motivated to work on
a robotic
platform and have strong computer programming skills.

References:

Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean
Ma,
Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander
C.
Berg and Li Fei-Fei, ImageNet Large Scale Visual Recognition Challenge,
arXiv:1409.0575, 2014.

Wang, X., Gupta, A., Unsupervised Learning of Visual Representations using
Videos,
arXiv:1505.00687v2, 2015.

Ciliberto, C., Smeraldi, F., Natale, L., Metta, G., Online Multiple
Instance Learning
Applied to Hand Detection in a Humanoid Robot, IEEE/RSJ International
Conference
on Intelligent Robots and Systems, San Francisco, California, September
25-30, 2011.



4) Learning to recognize objects using multimodal cues

Tutor: Lorenzo Natale, Lorenzo Rosasco

Description: robots can actively sense the environment using not only
vision but also
haptic information. One of the problems to be addressed in this case is how
to
control the robot to explore the environment and extract relevant
information (so
called exploratory procedures). Conventionally, learning to recognize
objects has
been primarily addressed using vision. However, physical properties of
objects are
more directly perceived using other sensory modalities. For this reason,
recent work
has started to investigate how to discriminate objects using other sensory
channels,
like touch, force and proprioception. The goals of this project are i) to
implement
control strategies for object exploration, investigating to what extent
different
explorative strategies contribute to object discrimination, ii) the
implementation of
learning algorithms that allow the robot to discriminate objects using
haptic
information and, finally, iii) to investigate how haptic information can be
integrated
with vision to build a rich model of the objects for better discrimination.

Requirements: This PhD project will be carried out within the Humanoid
Sensing and
Perception lab (iCub Facility) and Laboratory for Computational and
Statistical
Learning. The ideal candidate should have a degree in Computer Science or
Engineering (or equivalent) and background in Machine Learning, Robotics and
possibly in Computer Vision. He should also be highly motivated to work on
a robotic
platform and have strong computer programming skills.

References:

Pasquale, G., Ciliberto, C., Odone, F., Rosasco, L., and Natale, L.,
Teaching iCub to
recognize objects using deep Convolutional Neural Networks, in Proc. 4th
Workshop
on Machine Learning for Interactive Systems, 2015.

Liarokapis, M.V., Çalli, B., Spiers, A.J, Dollar, A.M., Unplanned,
model-free, single
grasp object classification with underactuated hands and force sensors,
IROS, 2015.

Madry, M., Bo, L., Kragic, D. and Fox, D., ST-HMP: Unsupervised
Spatio-Temporal
feature learning for tactile data, ICRA 2014.



12) Tera-Scale Machine Learning in a Dynamic World

Tutors: Giorgio Metta, Lorenzo Rosasco

Department: iCub Facility

Description: Machine learning methods have been applied with remarkable
success
to solve a variety of perceptual/cognitive tasks, e.g. in vision and
speech. Yet, current
state of the art algorithms are challenged by increasing amount of
high-dimensional
data, possibly evolving over time. The goal of this project is to tackle
the design and
deployment of sound machine learning methods to learn from large/huge scale
data
sets possibly acquired over time in changing conditions. This problem finds
natural
application in the context of humanoid robotics where data are constantly
made
available by sensory systems. The plan is to first consider extensions of
classical
convex approaches such as kernel methods, by incorporating ideas such as
sketching, hashing and randomization. The idea is then to further explore
possibly
non-convex, hierarchical (deep) learning methods, based on greedy
optimization
strategy. Emphasis will be on developing effective solutions, while keeping
optimal
theoretical guarantees.

Requirements: This PhD project will be carried out within the iCub Facility
and
Laboratory for Computational and Statistical Learning. The ideal candidate
should
have a degree in Computer Science or Engineering (or equivalent) and
background in
Machine Learning, Robotics and in optimization/signal processing. He should
also be
highly motivated to work on a robotic platform and have strong mathematical
and
or/ computer programming skills.

References:

Incremental Semiparametric Inverse Dynamics Learning, Raffaello Camoriano,
Silvio
Traversaro, Lorenzo Rosasco, Giorgio Metta, Francesco Nori (IROS 2016)

Less is More: Nyström Computational Regularization Alessandro Rudi,
Raffaello
Camoriano, Lorenzo Rosasco (NIPS 2015)

Contacts: Giorgio Metta (giorgio.metta at iit.it
<https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.iit.it_owa_redir.aspx-3FREF-3DCAwjVMAR7gaK8PBlBCVmHLjF9XYAYp5HOIaeQmO2qB3J4sjpoWjTCAFtYWlsdG86Z2lvcmdpby5tZXR0YUBpaXQuaXQ&d=CwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=o8hWZvpMaVyBNU6m87vr1I2HV0EW0SpR1Atec92ZeAE&s=TdLRv3Hkvj2kiNMs3HNaIsUPKTYx6LdvHBNlR0UaQ60&e= .>)
and Lorenzo Rosasco
(lrosasco at mit.edu
<https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.iit.it_owa_redir.aspx-3FREF-3DsgFTEyDDdn0YLf3a3XWI0oTipfkEAZkUYCwiUq-2DOunnJ4sjpoWjTCAFtYWlsdG86bHJvc2FzY29AbWl0LmVkdQ&d=CwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=o8hWZvpMaVyBNU6m87vr1I2HV0EW0SpR1Atec92ZeAE&s=T-p8KnkpKx_6DY9cJrahJrNXK_WuzrWvdoQ371bobKM&e= ..>
)

-----------------------------------------------------

Best Regards,
*Raffaello Camoriano <https://urldefense.proofpoint.com/v2/url?u=https-3A__it.linkedin.com_in_raffaellocamoriano_en&d=CwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=o8hWZvpMaVyBNU6m87vr1I2HV0EW0SpR1Atec92ZeAE&s=frRc8U8gVChRhp2u7YgpskUyzST2xygAKoYO9Eq_5Hc&e= >*


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