2019 International Summer School on Deep Learning for Robot Vision

From 9 Dec, 2019 18:33 until 13 Dec, 2019 20:33
Posted by f.agnew@ieee.org
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The IEEE RAS International Summer School on "Deep Learning for Robot
Vision" will be held on 9-13 December 2019 in Santiago & Rancagua, Chile.

Important information:
• The registration deadline is 31 October, 2019.
• The number of participants is limited to 200.
• No registration fees for students.
• The registration does NOT cover accommodation or meals.
• Applications sent by email will NOT be processed. Check the
registration webpage for detailed instructions:

This international Summer School targets students (Master / PhD level
and last years of undergraduate), researchers and professionals
interested in Robotics, Robot Vision, Deep Learning and related topics.
The official language of the summer school is English, and it will
include tutorial courses, keynote lectures, a student poster
competition, and live demonstrations such as autonomous vehicles,
domestic service robots, among others.

There are  no registration fees for students  regardless of their
nationality or country of affiliation, but priority will be given to
students with an IEEE

We expect to have over 100 attendees to the summer school (in 2012 we
hosted 85 attendees from over 15 countries).

A  travel grant program for international students will provide support
for international students attending the summer school. The application
process will open on 10 June 2019. For details of the travel grant program,
please check: http://robotvision2019.amtc.cl/index.php/travel-grant/

The Summer School is co-organized by the Advanced Mining Technology
Center of the Universidad de Chile and the Institute of Engineering
Sciences of the Universidad de O’Higgins, Chile. The summer school is
co-funded by the IEEE Robotics and Automation Society (RAS) Summer
School Program, technically sponsored by the IEEE RAS Technical
Committee on Robot Learning, and supported by the IEEE RAS Chilean
Chapter. This Summer School follows the successful  IEEE RAS Summer
School on “Robot Vision and Applications” organized in Chile in 2012.

The Summer School will provide a clear overview of Deep Learning methods
in Robotics with a particular emphasis in robot vision, while also
providing an in-depth analysis of state-of-the-art research in this
area. We will have introductory lectures and short advance courses in
the following topics: deep learning for robot vision, deep reinforcement
learning, deep learning for robot vision under time & hardware
constraints, deep learning for 3D reconstruction & SLAM, deep &
model-based learning, deep learning for manipulation & grasping, etc.

In addition to the tutorial courses and keynote lectures, we will have:
  • A student poster contest on robotics
  • Demo sessions where various robots will be displayed and introduced
  • A hands-on course on deep learning methods using a last-generation
high-performance computing platform (GPU cluster NVIDIA DGX-1). This
course is limited to ten attendees which will be selected based on a
project proposal.
  • Finally, a focused discussion session with some of the lectures of
the Summer School. This session is limited to ten participants.
Interested participants need to submit a topic or paper of their
interest to be discussed during the session.

The Summer School will have presentations by renowned international
speakers, including:
  • Nicholas Roy, Robust Robotics Group, CSAIL, MIT
  • Niko Sünderhauf, Australian Centre for Robotic Vision and Queensland
University of Technology (QUT) in Brisbane
  • Jens Kober, Cognitive Robotics department, Delft University of
  • Juxi Leitner, Australian Centre of Excellence for Robotic Vision
  • Stefan Leutenegger, Imperial College London

A detailed program will be made available at the Summer school website

If you want to be notified about important information regarding the
summer school such as when the program is made available, the
registration process opens, please fill your contact information in the
following form: https://goo.gl/forms/jKeAsqnLuFwZKWx83