PhD position in Robotics and Machine Learning for 2016
I have an open PhD position available in my group at Imperial College London:
Department: Dyson School of Design Engineering
Location: South Kensington campus, London, UK
Start date: 1st May 2016 (or soon after)
Duration: 3.5 years
Closing Date: 10 April 2016
Fully funded (all tuition fees paid) for UK/EU nationals, with additional stipend: 18,000 GBP per annum
While this position is also open to Overseas applicants, they will only be funded up to the UK/EU level, and will be expected to provide self-funding for the remaining tuition fees.
PhD Research Topic
The foundations of robotics and robot control were established at a time when there was very limited computational power available. Therefore, the robots’ design and control algorithms were simplified to extreme. Nowadays, we have at our disposal huge computational resources, but we still continue building and controlling robots based on the old concepts. For example, the assumption that the robot links are rigid bodies and that the pose of the end-effector can be calculated through simple forward kinematics by measuring the joint angles is still standard. Such assumptions lead to bulky and heavy robots because the links must be designed not to bend during operation. Even series-elastic actuation relies on the same assumption of rigid links.
The goal of this PhD research project is to investigate a radically new approach for controlling robots based on Machine Learning. Instead of using hand-made analytic models of a robot, the robot will learn its own model. Machine learning, including Deep Learning and Reinforcement Learning can be used to autonomously learn forward and inverse models of a robot’s kinematics and dynamics. Computer vision can be used to provide perception for both the environment and the robot’s own body. The ultimate goal would be the creation of a plug-and-play controller that works without any prior knowledge of the robot.
Such a solution offers tremendous potential to revolutionize the way we design and control robots, and to significantly expand their capabilities. For example, the robot links will no longer need to be so stiff, and the kinematics will no longer need to be fixed. As an illustration, imagine a lightweight prosthetic arm or a robot exoskeleton that can grow, bend, and adapt to accommodate its patient. Such a device would be impossible to control with the existing control methods. Another example is flexible use of tools, where the robot easily adapts its controller to use any new tool by online learning of the combined arm-plus-tool kinodynamics. Further applications are envisioned to soft robots (e.g. elephant trunk like robots) which are difficult to control with conventional approaches.
This research has the potential to lead to re-thinking of the established robot design paradigm (stiff links, fixed kinematics), since robot design and control are tightly coupled: the way we control robots determines the way we design them, and vice versa. Novel robot designs will be sought that leverage the rise of affordable 3D printing and novel smart materials, and could lead to the development of hybrid soft-hard robots, modular and reconfigurable robots (evolving hardware), self-repairing and self-improving robots, etc.
The funding for this PhD position is provided by Dyson Ltd. Their focus is on forward-looking research in robot perception and control with the goal of developing the breakthrough technology which will lie at the heart of new categories of robotic products for the home and beyond. Potential applications for the developed research will be sought in close collaboration with Dyson’s Robotics Research group.
The PhD student will be supervised by Dr Petar Kormushev at the Dyson School of Design Engineering, with possible co-supervision from the Dyson Robotics Lab at Imperial’s Department of Computing.
The Dyson School of Design Engineering which is the 10th and newest engineering department at Imperial College London. It was formed in July 2014, building on the long-standing design and engineering expertise at Imperial as well as the world-renowned Innovation Design Engineering (IDE) programme run jointly by Imperial and the Royal College of Art. The School has a fast growing population of both staff and students. It is located at the South Kensington campus of Imperial, right next to Hyde Park.
– You must have an MEng or MSc degree (or equivalent experience and/or qualifications) in an area pertinent to the subject area, i.e. Computing, Mathematics or Engineering.
– You must have a high standard undergraduate degree at UK 1st class or 2:1 level (or international equivalent)
– You must be fluent in spoken and written English and meet Imperial’s English standards.
– You must have excellent communication skills and be able to organise your own work and prioritise work to meet deadlines.
– The ideal candidate will have strong background in both Machine Learning and Robotics.
– Strong academic track record and practical software skills are desired.
– Any published scientific papers would be a plus.
How To Apply
All applications must be sent to Dr Petar Kormushev (p.kormushev [at] imperial.ac.uk) with the keyword “[PhD-2016-Imperial-Dyson]” in the subject field.
Applications must include the following:
– Full CV, with a list of any significant course projects and/or industrial experience;
– A 2-page research statement indicating what you see are interesting research issues relating to the above PhD topic description and why your expertise is relevant;
– Full academic transcripts/grades;
– A copy of all publications of the applicant (if any).
Selected applicants will be encouraged to submit a formal application online at: http://www.imperial.ac.uk/design-engineering/study/phd/
For any questions regarding the application process please contact Dr Petar Kormushev (p.kormushev [at] imperial.ac.uk).
Dr Petar Kormushev
Lecturer in Robotics and Computing
Dyson School of Design Engineering
Imperial College London
South Kensington, London, SW7 2AZ
Work phone: +44-20-75949235
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Syndicated 2016-03-02 14:28:41 from Petar Kormushev