Name: Petar Kormushev
Member since: 2010-09-25 15:46:41
Last Login: 2013-06-21 10:25:11
Petar Kormushev is a senior postdoctoral researcher at the
Advanced Robotics department of the Italian Institute of
Technology, doing research on the iCub humanoid robot and a
Barrett WAM robot.
In 2009 he graduated with a PhD degree in Computational
Intelligence from Tokyo Institute of Technology. His
research interests include robotics and machine learning,
especially reinforcement learning for intelligent robot
He holds a MSc degree in Artificial Intelligence, a MSc
degree in Bio- and Medical Informatics, and a BSc degree in
Computer Science from Sofia University. He has participated
in many scientific projects, including European INFRAWEBS
project for designing the future Semantic Web, and Japanese
NEDO project for developing a common basis for next-
He has won a number of prestigious awards, including a
“John Atanasoff” award, a “St. Kliment Ohridski” award, and
a 4-year Monbukagakusho/MEXT Japanese research fellowship.
Recent blog entries by Petar.Kormushev
Post-doc positions at Imperial College London
There are outstanding opportunities for becoming a postdoctoral researcher (PDRA) at Imperial College London. Here I have listed three of the highly-competitive funding schemes.
I am available to mentor potential Post-Doc applicants on research topics related to robotics and machine learning. Interested candidates should contact me by e-mail before submitting their application.
Imperial College JRF (Junior Research Fellowship)
- A competitive salary
- Research and travel expenses of up to £45,000
- Personal mentoring support from a senior Imperial academic
- The chance to take full responsibility for setting and directing your own research agenda
Royal Society URF (University Research Fellowship)
- 80% of the basic salary costs up to £39,389.64 in the first year, estates costs and indirect costs
- Research expenses (up to £13,000 for the first year and up to £11,000 annually thereafter)
RAEng Research Fellowship
- Freedom to concentrate on basic research in any field of engineering
- The services of a mentor to offer advice and to facilitate the formation of industrial links
UROP position at Imperial College London for 2016
If you are a full time undergraduate student at Imperial College London you are invited to apply to the Undergraduate Research Opportunities Programme (UROP).
The available UROP projects are advertised by the corresponding supervisors here:
Students can apply to receive a bursary (funding) for the duration of their UROP project. The bursary will provide the student with a contribution towards their living costs for 6-12 weeks while undertaking a research experience within Imperial College during the summer of 2016.
The deadline to submit an application for funding is 14 March 2016.
I am available to supervise undergraduate students for a UROP project on topics related to robotics and machine learning. Interested applicants should contact me by e-mail [p.kormushev (at) imperial.ac.uk].
UROP project description (tentative)
Title: Robotics and Machine Learning UROP
Description: Depending on the skills and interests of the student, this UROP project could include designing a new robot, creating it using 3D printing, and controlling it. The main focus is on novelty – coming up with a novel robot design, or novel robot controller, or novel way to manufacture a robot, such as a robot arm or a mobile robot. In terms of software, the focus is on applying Machine Learning methods for the flexible control of a robot, and to allow the robot to learn new skills from experience. The topic is quite flexible and will be defined in collaboration with the student.
Requirements: Basic knowledge of robotics, software programming skills, creativity.
How to apply
The scheme is now open for applications. Instructions for application:
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
The post PhD position in Robotics and Machine Learning for 2016 appeared first on Petar Kormushev.
PhD Scholarships at Imperial College London
The Imperial College PhD Scholarship Scheme offers an outstanding opportunity for potential PhD students.
If you are a high performing undergraduate or Master’s student, and have a strong desire to undertake a PhD programme at a world class research institution, you could be selected to receive full tuition fees and a generous stipend for a PhD place at Imperial College London.
Opportunities for PhD funding are extremely competitive. In the 2015-16 PhD admissions period, less than half of the eligible PhD applicants who nominated themselves for the IC PhD Scholarship were shortlisted by their chosen Department to be considered for this scheme, Imperial’s most prestigious award. Ultimately, only 19% of those who self-nominated were awarded the scholarship. Applicants should be confident that they are able to demonstrate outstanding academic performance before applying for this scholarship scheme.
The scheme aims to provide up to 50 research students with great potential the opportunity to work within their chosen research field with the support of an excellent supervisor.
The earliest start date for funded places is 1 August 2016, the latest start date is 1 November 2016.
Successful candidates will receive the following financial support for up to 3.5 years:
- Full funding for tuition fees
- A stipend of £20,600 per annum to assist with living costs
- A consumables fund of £2,000 per annum for the first 3 years of study
Applications put forward for this scholarship scheme will be considered throughout the academic year.
- Applicants who apply before 29 January 2016 and are awarded a scholarship will be notified by 23 March 2016.
- Applicants who apply before 1 April 2016 and are awarded a scholarship will be notified by 27 May 2016.
I am available to supervise PhD students on topics related to robotics and machine learning. Interested applicants should contact me by e-mail before submitting their PhD application.
How to apply
The scheme is now open for applications. Instructions for application:
Online Regeneration of Bipedal Walking Gait Optimizing Footstep Placement and Timing
The video presents experiments during which a humanoid robot is subjected to external pushes and recovers stability by changing the step placement and duration.
It starts from showing effectiveness of the feedback controller during stepping in place. Then it continues to present how the developed algorithm regenerates the step placement and duration to regain stability after lateral pushes. It concludes with showing how the algorithm works during forward locomotion.
Przemyslaw Kryczka, Petar Kormushev, Nikos Tsagarakis, Darwin G. Caldwell, “Online Regeneration of Bipedal Walking Gait Optimizing Footstep Placement and Timing”, In Proc. IEEE/RSJ Intl Conf. on Intelligent Robots and Systems (IROS 2015), Hamburg, Germany, 2015.
Publication PDF: http://kormushev.com/papers/Kryczka_IROS-2015.pdf
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