[robotics-worldwide] [jobs] Second call for a PhD position at the Max Planck Institute for Biological Cybernetics, Tuebingen, Germany

Aamir aahmad at isr.ist.utl.pt
Mon Mar 24 03:48:48 PDT 2014

Apologies if you receive multiple copies of this announcement.

*[Important note]* Please be advised that the deadline to apply for this
PhD position is *31st March 2014*.

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

*Motivation*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.

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.

 *Summary of Global Objectives*
 Expected objectives of this PhD thesis are:

   - 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.
   - 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

*Expected Qualifications and Skills of the Candidate*

   - 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.
   - The candidate should have fluent command of English as a written and
   spoken language.
   - Prior experience in computer vision and image processing is essential.
   - The candidate must have excellent programming skills in one or more
   languages, e.g., C, C++ and python.
   - Knowledge and experience in Robot Operating System (ROS) will be a

*Selection Procedure*
Interested candidates who meet the above mentioned requirements should send
the following documents (all in pdf format) to aahmad at isr.ist.utl.pt by
31st March, 2014

   - Motivation letter.
   - Curriculum Vitae (Including a list of publications)
   - Online link to their own code snippets or softwares developed (these
   can be inserted as a section in the CV).
   - 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)
   - Copy of the last diploma and transcripts (grade sheet).

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.

*Other Information*

Homepage of Max Planck Institute for Biological Cybernetics (MPI-KYB) at
Tübingen, Germany.

Homepage of Institute for Systems and Robotics, Lisbon, Portugal

*Brief Description of Work*

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.

*Highly Scalable Sensor Fusion*

 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

*Implicit-and-Explicit Interaction*

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.

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.

*Case Studies*

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.

Kind regards
Aamir Ahmad
Postdoctoral Researcher
Institute for Systems and Robotics,
Instituto Superior Técnico, Lisbon, Portugal


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