<div dir="ltr"><div><div><div><div><div><div>Apologies if you receive multiple copies of this announcement.<br><br></div><div><b>[Important note]</b> Please be advised that the deadline to apply for this PhD position is <b>31st March 2014</b>. <br>
</div><div><b><br>Foreword</b> <br>The
Max Planck Society (MPS) is Germany's most successful research
organization. Since its establishment in 1948, it has produced 17 Nobel
laureates from the ranks of its scientists, putting it on a par with the
best and most prestigious research institutions worldwide. Max Planck
Institutes are built up solely around the world's leading researchers.
The Max Planck Institute for Biological Cybernetics (MPI-KYB) at
Tübingen, Germany, is one of the 82 independently organized research
facilities of the MPS that carry out basic research in the service of
the general public in the fields of natural sciences, life sciences,
social sciences, and humanities. The Department of Human Perception,
Cognition and Action at MPI-KYB, directed by Prof. Dr. Heinrich H.
Bülthoff, studies human perception with the help of virtual reality
(VR). This enables the experiments to be conducted in a controlled and
yet natural surroundings for which there are special hardware and
experimental constructions. Among the several state-of-the-art
laboratories at MPI-KYB is the Cyberneum, a VR research facility
equipped with several sophisticated VR systems that provide unique
opportunities to study human perception and human-machine interactions.
The Autonomous Robotics and Human-Machine Systems (AR-HMS) group, within
the department of Human Perception, Cognition and Action at MPI-KYB,
opens a PhD position for motivated candidates with excellent
qualifications. <br>
<br><b>Motivation<br></b>As robots increasingly become a part of daily
life in our society, there is a preeminent need for the robots to be
able to perceive humans in a more implicit manner in order to make
human-robot interaction as natural as possible. For instance, in crowded
urban environments, robots must use their own vision (or a network of
sensors in the environment) to distinguish between those humans who
require the robot's attention for some cooperative/collaborative purpose
and those humans who simply occupy the same environment for other
possible reasons. Such visual classification based on gesture
recognition, emotion detection, etc., should precede any subsequent
direct interaction (e.g., verbal) between the robot and a human in order
to make the overall human-robot interaction natural and less
complicated for the untrained human users of the robots. Simultaneously,
diverse vision-based functionalities in robots are essential to
accomplish complex tasks by human-robot or robot-only teams that involve
interaction and/or collaboration. Such functionalities can range from
simpler ones, e.g., single object or person detection, recognition and
tracking using a single static camera to more complex ones, e.g.,
tracking multitude of people in crowded and highly dynamic environments
and at the same time perceiving the emotional response of humans with
whom the robot is directly interacting. Thanks to networked robot
systems (NRS), presence of multiple mobile sensors (e.g., micro aerial
vehicles equipped with camera) or static sensors (e.g., wall/ceiling
mounted network cameras) provide a strong foundation to tackle such
complex functionalities for real time applications. The focus of this
thesis will, therefore, be on the issues of scalability and real time
applicability of multiple vision-based functionalities in an NRS where
human-robot interaction is one of the most essential components.<br>
<br><b>Keywords</b><br>Sensor fusion, Cooperative perception, Person
tracking; detection and tracking from non-inertial frames; face and
gesture recognition; stereo-vision systems; motion capture systems;
human-robot interaction, multi-robot systems. <br>
<br> <b>Summary of Global Objectives</b><br> Expected objectives of this PhD thesis are:<br><ul><li>To
conceptualize and develop a framework that hierarchically integrates
person detection, classification and tracking with face and gesture
recognition, within an NRS that consists of human-sized mobile robots,
micro aerial vehicles, static sensors in the environment and humans
cooperating with the robot in an urban environmental setting. Primary
focus will be on indoor scenarios.</li><li>To conceptualize and develop novel algorithms for the vision-based
functionalities embedded within the above mentioned framework. The major
focus here will be on the scalability issues of those algorithms such
that they are applicable to extremely large environments consisting of a
high number of static and mobile sensors. Applicability refers to
computational feasibility in real time while simultaneously maintaining
optimality of the solution.</li></ul><b><br>Expected Qualifications and Skills of the Candidate</b><br><ul><li>We
seek highly qualified candidates with a master degree in one of the
following broad areas: robotics, mechanical engineering, electrical
engineering, computer science or other related fields. <br>
</li><li>The candidate should have fluent command of English as a written and spoken language.</li><li>Prior experience in computer vision and image processing is essential.</li><li>The candidate must have excellent programming skills in one or more languages, e.g., C, C++ and python. <br>
</li><li>Knowledge and experience in Robot Operating System (ROS) will be a plus.<br></li></ul><b>Selection Procedure</b><br>Interested candidates who meet the above mentioned requirements should send the following documents (all in pdf format) to <a href="mailto:aahmad@isr.ist.utl.pt" target="_blank">aahmad@isr.ist.utl.pt</a> by 31st March, 2014<br>
<ul><li>Motivation letter. <br></li><li>Curriculum Vitae (Including a list of publications)</li><li>Online link to their own code snippets or softwares developed (these can be inserted as a section in the CV).</li><li>A
2-page summary of their master thesis or any other research results
(which they consider as their most important results) (Bibliography
should not be within these 2-page limit)</li><li>Copy of the last diploma and transcripts (grade sheet).<br></li></ul>Selected
candidate will be expected to enroll in the PhD program in the
beginning of September 2014 at the University of Tübingen and will carry
out their research work at Max Planck Institute for Biological
Cybernetics, Tübingen. However, prior to the PhD enrollment, the
candidate will be expected to undertake an additional research
internship at the Institute for Systems and Robotics in Instituto
Superior Técnico, Lisbon. The internship is foreseen for a period of 3-4
months starting around May 2014.<br>
<br><b>Other Information</b><br><br>Homepage of Max Planck Institute for Biological Cybernetics (MPI-KYB) at Tübingen, Germany.<br><a href="http://www.kyb.tuebingen.mpg.de/" target="_blank">http://www.kyb.tuebingen.mpg.de/</a><br>
<br>
Homepage of Institute for Systems and Robotics, Lisbon, Portugal<br><a href="http://welcome.isr.ist.utl.pt/home/" target="_blank">http://welcome.isr.ist.utl.pt/home/</a><br><br><b>Brief Description of Work</b><br> <br>In
the context of this PhD thesis work, a Network Robot System (NRS) will
consist of i) a mobile robot with an omni-directional chassis equipped
with vision sensors and simple actuators (arm/gripper), ii) multiple
micro aerial vehicles (MAVs), and iii) static sensors fixed within the
environment, e.g., network cameras. <br>
<br><i>Highly Scalable Sensor Fusion</i><br> <br> To achieve robust
vision-based functionalities through an NRS, one needs to perform
optimal sensor fusion. However, as environments scale up in size and
feature-richness, the amount of visual information that needs to be
processed becomes overwhelmingly high. Consequently, performing sensor
fusion optimally and in real-time becomes exponentially heavy. One good
example is how the number of particles required by a particle
filter-based (an approximately optimal technique) object tracker grow
exponentially with the increase of the state space dimension to maintain
a given accuracy of the tracker. Nevertheless, there are possible ways,
e.g, exploiting dependencies between state variables, through which an
increase in computational complexity can be restricted. In this PhD
work, such techniques will be explored to develop highly scalable sensor
fusion algorithms.<br>
<br><i>Implicit-and-Explicit Interaction</i><br> <br>Another major
focus of this work is to investigate methods for implicit human-robot
interaction. Here, implicit interaction refers to embodied communication
between humans and robots. Robots' understanding of human body/hand
gestures, visual cues and human emotions based on facial expressions and
body posture are among some forms of embodied communication that would
eventually make human-robot interaction more fluid and natural. To this
end, state-of-the-art vision-based techniques will be investigated for
human body/hand gestures and emotion detection. Indeed, taking advantage
of an NRS will facilitate the detection process, however, innovative
algorithms must be developed for fusing visual information through
various sensors available in the environment for this purpose. <br>
<br>On the other hand, explicit interaction between humans and robots
involve activities such as voice-based communication, touch screen-based
communication, etc. Humans naturally use both implicit and explicit
form of communication in a general interaction. To this effect, fusion
of visual information with that obtained through speech (microphones)
and touch (touch screen) will be made. A hierarchical information fusion
architecture will form the backbone of such human-robot interaction
method.<br>
<br><i>Case Studies</i><br> <br>Real robot implementation of the
algorithms developed during this PhD thesis will be made in the
following contexts: i) A domestic service robot (with an
omni-directional mobile base) assisting an elderly person at home where
the home environment will consist of static sensors as well as multiple
MAVs with on-board sensors. ii) A service robot (same platform as in the
first case study) assisting shoppers in a supermarket where the
environment consists of several other robots of the same kind, multiple
MAVs and static sensors.<br>
<br><br></div>Kind regards<br></div>Aamir Ahmad<br></div>Postdoctoral Researcher<br></div>Institute for Systems and Robotics,<br></div>Instituto Superior Técnico, Lisbon, Portugal<br></div><br><a href="http://www.aamirahmad.com" target="_blank">www.aamirahmad.com</a><br>
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