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PhD positions in Robotics and Machine Learning for 2016

Call for PhD students for 2016
PhD Program in Bioengineering and Robotics
University of Genoa, jointly with the Italian Institute of Technology (IIT)


PhD positions with scholarships are available at the Italian Institute of Technology (IIT)
Location: Genoa, Italy
Starting date: November 2015
Application deadline: June 10th, 2015, noon (Italian time: GMT+2)

Please note that IIT is an English-language research institute, so it is not required to speak Italian.

IIT has state-of-the-art facilities and has rapidly established itself among top research institutes worldwide. IIT has a strong international character, with more than 40% foreign scientific staff drawn from over 50 countries worldwide.

Useful links:

I have 4 PhD student positions open in my lab (Robot Learning and Interaction Lab) under Themes 26, 27, 28 and 29 in the area of Robot Learning as described below.

 


THEME 26. Robotic Surgery with Improved Safety using Machine Learning for Intelligent Robot Tele-operation and Partial Autonomy

Tutors: Dr. Petar Kormushev, Prof. Darwin G. Caldwell


Robotic surgery

Description: Flexible hyper-redundant systems are becoming of increasing interest in medical applications where the flexibility of the robot can be used to direct the surgery around delicate tissues, however, these system are highly non-linear with complex dynamic making them very difficult to control.

This project will develop and implement machine learning algorithms to improve the intelligence of control and perception in flexible devices and enhance safety.

The advantages of using machine learning will be investigated in multiple potential areas, as follows: in low-level robot control using model learning approaches; in feedback control considering multi-modal input from position, force and pressure sensors; in tele-operation using learning of context-dependent skills for assisting the human operators (surgeons).

The work will also investigate the possibility of using partial autonomy at a lower control level using reactive strategies for robot control. With respect to safety the project will consider how to use the development of learning algorithms to automatically detect
abnormalities during robot teleoperation. These abnormalities may include excessive forces/pressure, excessive bending, unusual signals potentially indicating problems during the medical procedure.

Requirements: background in computer science, mathematics, engineering, physics or related disciplines.

 


THEME 27. Novel Robot Control Paradigms enabled by Machine Learning for Intelligent Control of the Next Generation Compliant and Soft Robots

Tutors: Dr. Petar Kormushev, Prof. Darwin G. Caldwell


pancake subclip - finally learned skill

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.

Requirements: background in computer science, mathematics, engineering, physics or related disciplines.

 


THEME 28. Agile Robot Locomotion using Machine Learning for Intelligent Control of Advanced Humanoid Robots

Tutors: Dr. Petar Kormushev, Dr. Nikos Tsagarakis


Blue_COMAN_robot_standing_proudly_at_IIT

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.

Requirements: background in computer science, mathematics, engineering, physics or related disciplines.

 


THEME 29. Dexterous Robotic Manipulation using Machine Learning for Intelligent Robot Control and Perception

Tutors: Dr. Petar Kormushev, Prof. Darwin G. Caldwell


Teaching HOAP-2 to erase the whiteboard

Description: This project will investigate collaborative human-robot task learning and execution that uses the available perception (particularly tactile). The work will develop algorithms for learning of collaborative skills by direct interaction between a non-expert user and a robot. The tasks will build the necessary control algorithms to allow effortless and safe physical human-robot interaction using the available tactile feedback.

The final objectives will include: acquiring the perceptual information needed for robot to co-manipulate an object with human, understanding human’s state in an interaction task so as to react properly, building a framework for online compliant human-robot interaction based on real-time feedback of the state of the object and human.

The project will also consider semi-supervised and unsupervised skill learning approaches. It will develop tactile-guided autonomous learning algorithms based on state-of-the-art methods for reinforcement learning and deep learning. The tactile feedback will help to increase the performance of skill execution autonomously by the robot through trial-anderror interactions with the objects in the environment.

In addition this work will focus on supervised skill learning approaches. It will develop tactile-guided learning algorithms based on state-of-the-art methods for learning by imitation and visuospatial skill learning. The tactile perception information will be used both in the learning phase and the execution phase, to improve the robustness and the range of motor skill repertoire.

Requirements: background in computer science, mathematics, engineering, physics or related disciplines.

 


Department: ADVR (Department of Advanced Robotics) http://www.iit.it/advr

References: Please check my Publications page.

Contact: petar.kormushev(a)iit.it