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About me

Notice: I moved to Imperial College London in Sep 2015!
Petar Kormushev

My name is Petar Kormushev. I am a Lecturer (equiv. to: Assistant Professor) at Imperial College London, UK. I teach robotics and computing at the Dyson School of Design Engineering. I am also a Visiting Senior Research Fellow at King’s College London, UK.

Previously, I was a Research Team Leader at the Advanced Robotics department of the Italian Institute of Technology (IIT) in Genoa, Italy.

I do research in robotics and machine learning. My research focus is on reinforcement learning algorithms and their application to autonomous robots. My long-term goal is to create robots that can learn by themselves and adapt to dynamic environments. You can find details about my Research and Publications, as well as watch Videos from my research experiments with robots.

At IIT, I am leading the Learning and Interaction Lab of the Advanced Robotics department. We develop machine learning algorithms and apply them to robots like the compliant humanoid robot COMAN, the iCub humanoid robot, the Barrett WAM arm manipulator robot, and the Fujitsu HOAP-2 small humanoid robot. This is my page at IIT.

In 2009, I obtained my PhD degree from Tokyo Institute of Technology (Japan), where I was doing research in computational intelligence under the supervision of Prof. Kaoru Hirota. My PhD thesis was dedicated to methods for speeding up the reinforcement learning process. I was also supervised by Visiting Prof. Kohei Nomoto from Mitsubishi Electric Corp. and Visiting Prof. Shigeaki Sakurai from Toshiba Corp.

In 2005, I received my MSc degree in Artificial Intelligence from Sofia University, at the Faculty of Mathematics and Informatics. In 2006, shortly before moving to Japan, I successfully defended my second MSc degree in Bio- and Medical Informatics.

I have had the chance to collaborate with many excellent researchers in robotics and machine learning, including Dr. Sylvain Calinon and Dr. Nikos Tsagarakis from IIT, Prof. Dragomir N. Nenchev from Tokyo City University, Assoc. Prof. Gennady Agre from Bulgarian Academy of Sciences, and Dr. Barkan Ugurlu from Toyota Technological Institute. I am deeply grateful to my university lecturer Assoc. Prof. Maria Nisheva, for inspiring me to pursue studies in artificial intelligence.

In addition to my scientific research, I have more than 7 years of working experience. In 2008, I worked at Google Japan for 3 months as a software engineer in the Search Quality team. I created a prototype of a new search query categorization system (which I called Google Genus) using machine learning algorithms.

Thanks for visiting my website, I wish you pleasant surfing!

— Petar

Open positions in my team

Post-doc positions at Imperial College London for 2016 (NEW!)
PhD position in Design Engineering for 2016 (NEW!)
PhD position in Robotics and Machine Learning for 2016
PhD scholarships at Imperial College London for 2016
UROP position (Undergraduate Research Opportunities Programme) at Imperial College London for 2016
PhD positions in Robotics and Machine Learning for 2016 (deadline passed)
PhD positions in Robotics and Machine Learning for 2015 (deadline passed)
PhD positions in Robotics and Machine Learning for 2014 (deadline passed)
Post-doc position in Machine Learning for Robotics, May 2013 (deadline passed)
Post-Doc positions in Robotics at IIT for 2012 (deadline passed)
PhD positions in Robotics at IIT for 2012 (deadline passed)

Moving to Imperial College London!

I am glad to announce that I will be moving to Imperial College London starting from September 2015!

My new position will be Lecturer in Robotics and Computing at the Dyson School of Design Engineering.

This is my new page at Imperial.

I will also continue supervising PhD students at the Robot Learning and Interaction Lab  which I was leading until now at Istituto Italiano di Tecnologia (IIT).

Visiting Researcher at KCL

Since August 2014 I am a Visiting Senior Research Fellow at KCL (King’s College London). I am working with the groups of Prof. Kaspar Althoefer and Prof. Maria Fox.
We are also collaborating on two EU FP7 projects (PANDORA and STIFF-FLOP) on the topics of persistent autonomy for underwater vehicles and learning for robot-assisted surgery.


Special Issue on Humanoid Robotics

I am co-editing a special Issue on Humanoid Robotics for the Advanced Robotics journal of RSJ. The submission deadline for papers is:   April 14, 2014

More information about this special issue

With the President of Bulgaria, Mr. Rosen Plevneliev

With the President of Bulgaria, Mr. Rosen Plevneliev

I received the 2013 John Atanasoff award by the President of Bulgaria!


“John Atanasoff” Award, 2013
Awarded by the President of Bulgaria for scientific excellence and contributions to the development of Information and Communications Technologies (ICT) in Bulgaria and abroad. The award bears the name of Prof. John Atanasoff (who is of Bulgarian descent) – the inventor of the first electronic digital computer.

More info here


In 2012, I was appointed as a co-chair of the prestigious IEEE RAS Technical Committee on Robot Learning!

“After three very successful years for the Technical Committee on Robot Learning, the founding chairs Jan Peters, Jun Morimoto, Russ Tedrake and Nicholas Roy are stepping down as chairs of the committee. They will be replaced by Petar Kormushev, Edwin Olson, Ashutosh Saxena, and Wataru Takano who have kindly agreed to take the reigns of the committee.”

WAM_visuospatial_skill_learning_150px[Oct, 2013] “Visuospatial skill learning” – a novel approach for robot learning by demonstration (visual imitation learning).
[Aug, 2011] We presented our pizza-making robot at the Robotic Challenge event of AAAI’2011 in San Francisco!

See more info and photos here.

[Mar, 2011] New conference paper was accepted to IROS 2011!Kormushev, P., Ugurlu, B., Calinon, S., Tsagarakis, N., and Caldwell, D.G., “Bipedal Walking Energy Minimization by Reinforcement Learning”, IROS 2011.
Robot learns to clean a whiteboard[Aug, 2010] My new research experiment: Robot learns to clean a whiteboard! A free-standing Fujitsu HOAP-2 humanoid robot learns to clean a surface by upper-body kinesthetic teaching.
Publication: P. Kormushev, D. Nenchev, S. Calinon, D. Caldwell, Upper-body Kinesthetic Teaching of a Free-standing Humanoid Robot, IEEE Intl. Conf. on Robotics and Automation (ICRA 2011), 2011.
Haptic input used to demonstrate force profile of a task[Nov, 2010] New journal paper! Kormushev, P., Calinon, S., and Caldwell, D.G., “Imitation learning of positional and force skills demonstrated via kinesthetic teaching and haptic input”, Advanced Robotics, 2011.
Demonstrating the ironing task[Nov, 2010] New conference workshop paper!
Kormushev, P., Calinon, S., and Caldwell, D.G., “Approaches for Learning Human-like Motor Skills which Require Variable Stiffness During Execution”, Workshop on Humanoid Robots Learning from Human Interaction (Humanoids 2010), 2010.
Robot archer[May, 2010] My new research experiment: Robot archer! An iCub humanoid robot learns the skill of archery: it learns to aim and shoot arrows at the center of the target. Check it in the Videos section.

The paper publication: “Learning the skill of archery by a humanoid robot iCub”, IEEE Intl. Conf. on Humanoid Robots (Humanoids 2010), pp. 417-423, 2010.

Pancake flipping robot[Jan, 2010] My new research experiment: Pancake flipping robot! A Barrett WAM robot learns how to flip pancakes by itself, trying to imitate a demonstration done by a human teacher.

Watch it in the Videos section.




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 five 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

EPSRC Fellowships


  • EPSRC has defined three career stages (postdoctoral, early and established career) and the attributes expected at each stage.
  • Applications can be submitted at any time and will be processed on a rolling basis at a review panel.

Newton International Fellowships


  • This scheme is for non-UK scientists who are at an early stage of their research career and wish to conduct research in the UK.
  • Eligibility requirements: to have a PhD, no more than 7 years of full-time postdoctoral experience, work outside the UK, and not hold UK citizenship

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.

More information:


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
In close collaboration with Dyson Robotics Lab
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 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




WALK-MAN robot

WALK-MAN robot

WALK-MAN is a humanoid robot developed by the Italian Institute of Technology and University of Pisa in Italy, within the European funded project WALK-MAN (www.walk-man.eu). The project is a four-year research programme which started in October 2013 and aims to developing a humanoid robot for disaster response operations.

WALK-MAN is the acronym of “Whole Body Adaptive Locomotion and Manipulation” underlining its main research goal: enhancing the capabilities of existing humanoid robots, permitting them to operate in emergency situations, while assisting or replacing humans in civil damaged sites including buildings, such as factories, offices and houses. In such scenarios, the Walk-man robot will demonstrate human type locomotion, balance and manipulation capabilities. To reach these targets, Walk-man design principles and implementation relied on the use of high performance actuation systems, compliant body and soft under actuated hand designs taking advantage of the recent developments in mechanical design, actuation and materials.

The first prototype of the WALK-MAN robot will participate in the DARPA Robotics Challenge finals in June 2015, but it will be further developed both in hardware and software, in order to validate the project results through realistic scenarios, consulting also civil defense bodies. The technologies developed within Walk-man project have also a wide range of other applications, including industrial manufacturing, co-worker robots, inspection and maintenance robots in dangerous workspaces, and may be provided to others on request.

Technical details

The prototype WALK-MAN platform is an adult size humanoid with a height of 1.85m an arm span of 2m and a weight of 118Kg. The robot is a fully power autonomous, electrically powered by a 2KWh battery unit; its body has 33 degrees of freedom (DOF) actuated by high power electric motors and all equipped with intrinsic elasticity that gives to the robot superior physical interaction capabilities.

The robot perception system includes torque sensing, end effector F/T sensors, and a head module equipped with a stereo vision system and a rotating 3D laser scanner, the posture of which is controlled by a 2DOF neck chain. Extra RGB-D and colour cameras mounted at fixed orientations provide additional coverage of the locomotion and manipulation space. IMU sensors at the head and the pelvis area provide the necessary inertial/orientation sensing of the body and the head frames. Protective soft covers mounted along the body will permit the robot to withstand impacts including those occurred during falling incidents. The software interface of the robot is based on YARP (www.yarp.it) and ROS (www.ros.org).

The WALK-MAN team information is available on the DARPA DRC website: