================================================================================= Department of Advanced Robotics Italian Institute of Technology (IIT) and University of Genoa, Italy *** PhD positions in Robot Learning for 2015 *** PhD positions with FULL SCHOLARSHIPS are available at the Italian Institute of Technology (IIT). Research area: Machine learning applied to robotics in general, and in particular to humanoid robots Location: Genoa, Italy Starting date: ** November 2014 ** Application deadline: *** August 30, 2014 *** at 12:00 noon (Italian time/CET)
Information about the relevant themes (Theme 21 and 22) <<<
http://kormushev.com/news/phd-positions-in-robotics-and-machine-learning-for-2015/
Online application here <<<
http://phd.dibris.unige.it/biorob/
Department: ADVR (Department of Advanced Robotics, IIT) <<<
http://www.iit.it/advr Please note that IIT is an English-language research institute, so it is NOT required to speak Italian. There are two PhD positions open for Themes 21 and 22. Both are in the area of Robot Learning, as described below. ----------------------------------------------------- THEME 21. Robot Learning of Motor Skills Tutors: Dr. Petar Kormushev, Prof. Darwin G. Caldwell Description: Despite the significant mechatronic advances in robot design, the motor skill repertoire of current robots is mediocre compared to their biological counterparts. Motor skills of humans and animals are still utterly astonishing when compared to robots. This PhD theme will focus on machine learning methods to advance the state-of-the-art in robot learning of motor skills. The type of motor skills that will be investigated include object manipulation, compliant interaction with objects, humans and the environment, force control and vision as part of the robot learning architecture. The creation of novel, high-performance, passively-compliant humanoid robots (such as the robot COMAN developed at IIT) offers a significant potential for achieving such advances in motor skills. However, as the bottleneck is not the hardware anymore, the main efforts should be directed towards the software that controls the robot. It is no longer reasonable to use over-simplified models of robot dynamics, because the novel compliant robots possess much richer and more complex dynamics than the previous generation of stiff robots. Therefore, new solutions should be sought to address the challenge of compliant robot control. Ideas from developmental robotics will be considered, in search for a qualitatively better approach for controlling robots, different than the currently predominant approach based on manually-engineered controllers. The work within this PhD theme will include developing novel robot learning algorithms and methods that allow humanoid robots to easily learn new skills. At the same time, the methods should allow for natural and safe interaction with people. To this end, the research will include learning by imitation and reinforcement learning, as well as human-robot interaction. ----------------------------------------------------- THEME 22. Robot Learning for Agile Locomotion Tutors: Dr. Petar Kormushev, Dr. Nikos Tsagarakis Description: The state-of-the-art high-performance, passively-compliant humanoid robots (such as the robot COMAN developed by IIT) offer a significant potential for achieving more agile robot locomotion. At this stage, the bottleneck is not the hardware anymore, but the software that controls the robot. It is no longer reasonable to use over-simplified models of robot dynamics, because the novel compliant robots possess much richer and more complex dynamics than the previous generation of stiff robots. Therefore, a new solution should be sought to address the challenge of compliant humanoid robot control. In this PhD theme, the use of machine learning and robot learning methods will be explored, in order to achieve novel ways for whole-body compliant humanoid robot control. In particular, the focus will be on achieving agile locomotion, based on robot self-learned dynamics, rather than on pre-engineered dynamics model. The PhD candidates will be expected to develop new algorithms for robot learning and to advance the state-of-the-art in humanoid robot locomotion. The expected outcome of these efforts includes the realization of highly dynamic bipedal locomotion such as omni-directional walking on uneven surfaces, coping with multiple contacts with the environments, jumping and running robustly on uneven terrain and in presence of high uncertainties, demonstrating robustness and tolerance to external disturbances, etc. The ultimate goal will be achieving locomotion skills comparable to a 1.5 - 2 year-old child. ----------------------------------------------------- Paper Reference: P. Kormushev, S. Calinon, D.G. Caldwell. Reinforcement Learning in Robotics: Applications and Real-World Challenges. MDPI Journal of Robotics (ISSN 2218-6581), Special Issue on Intelligent Robots, vol.2, pp.122-148, 2013. Contact: petar.kormushev@iit.it To apply please send a detailed CV, a statement of motivation, two reference letters, degree certificates, grades transcripts and other support materials to Petar Kormushev (petar.kormushev@iit.it). In addition, the applicants should fill the online application and upload the necessary application documents as described at the following site: (http://phd.dibris.unige.it/biorob/) no later than the 30th August 2014. International applications are encouraged and will receive support with visa issues, etc.
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Posted by: Tri Kurniawan Wijaya <trikurniawanwijaya@yahoo.com>
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