IEEE RAS TEP on “Deep Learning for Robot Vision”

 

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The IEEE RAS TEP on “Deep Learning for Robot Vision” was held on December 9-13, 2019 in Santiago & Rancagua, Chile. The Summer School was co-organized by the Advanced Mining Technology Center of the Universidad de Chile, Chile and the Institute of Engineering Sciences of the Universidad de O’Higgins, Chile. It was co-funded by the IEEE Robotics and Automation Society (RAS) Summer School Program, it was technically sponsored by the IEEE RAS Technical Committee on Robot Learning, and it follows the successful IEEE RAS Summer School on “Robot Vision and Applications” organized in Chile in 2012.

This international Summer School targeted 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 was English, and included tutorial courses, keynote lectures, a student poster competition, and live demonstrations (autonomous vehicle, robot soccer, domestic robots, etc.).

The Summer School provided 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. There were 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.

There were 13 keynote speakers:

Niko Sünderhauf

Australian Centre for Robotic Vision and Queensland University of Technology (QUT) in Brisbane

Keynote:  Introductory lecture on deep learning for robot vision

Talk A:  Semantic SLAM

Talk B: The Importance of Uncertainty for Deep Learning in Robotics

Jens Kober

Cognitive Robotics department, Delft University of Technology (TU Delft) 

Keynote: (Deep) Reinforcement Learning for Robotics

Talk: Learning State-Representations

Juxi Leitner

Australian Centre of Excellence for Robotic Vision

Keynote: (Deep) Learning for Robotic Grasping and Manipulation

Stefan Leutenegger

Imperial College London

Keynote: Spatial AI for mobile robots

Wei Pan

Delft University of Technology (TU Delft)

Keynote: Sparse Bayesian (Deep) Learning for Robotic

Pedro Maldonado

Universidad de Chile

Talk: Similarities and differences between artificial intelligence and the human brain

Wolfhart Totschnig

Universidad Diego Portales

Talk: Introduction to the ethics of artificial intelligence

Rodrigo Verschae

Universidad de O’Higgins

Talk: Deep Photovoltaic Prediction

Alexandre Bergel

Universidad de Chile

Talk: Building neural networks through neuroevolution

Javier Ruiz-del-Solar

Universidad de Chile

Nicolás Cruz & Javier Ruiz del Solar Talk: Bridging the simulation-to-reality-gap using generative neural networks

Francisco Leiva & Javier Ruiz del Solar : Deep Reinforcement Learning for Robotic Navigation and Collision Avoidance

Maria Jose Escobar

Universidad Tecnica Federico Santa Maria

Keynote: Towards a Chilean Artificial Intelligence Strategy

Nicolás Cruz

Universidad de Chile

Nicolás Cruz & Javier Ruiz del Solar Talk: Bridging the simulation-to-reality-gap using generative neural networks

Francisco Leiva

Universidad de Chile

Francisco Leiva & Javier Ruiz del Solar Talk: Deep Reinforcement Learning for Robotic Navigation and Collision Avoidance

 

Each year, the IEEE Robotics & Automation Society offers financial support for three Technical Education Programs (RAS-TEP), formerly Summer Schools. In efforts to bring RAS closer to its membership, these programs rotate though the Americas, Europe and Asia and Pacific.

The RAS-TEP program is jointly run by the Member Activities Board (MAB) and the Technical Activities Board (TAB). The program is intended to sponsor or co-sponsor up to three summer schools per year around the world. For more information on RAS Technical Education Programs and how to propose a new program, click here.

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