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Funded PhD Opportunity

Learning by Doing – teaching a robot complex skills

Subject: Computer Science and Informatics


Summary

Continuous advancements in robotics show that robots are being increasingly equipped with complex skills to solve a variety of problems. These skills are the result of research being conducted in laboratories and companies, crafted by developers and tested in sample environments. However, unlike a computer program, a robot has to operate in a world where the possibilities are potentially infinite and where it has to continuously adapt its basic programmed skills to face previously unforeseen situations. Unfortunately, there has been no successful development of robots that are autonomously capable of improving or adapting the basic skills with which they were initially equipped – something that comes natural to humans.

In this project, we propose to address these problems by developing a new skills building framework that allows a robot to successfully complete complex tasks by using previously learned primitive actions obtained from a skills library that resides in the Cloud. This library will be populated with primitive actions, such as grasping or object manipulation, from many robot sources, therefore vastly increasing the knowledge available for robots when faced with an unknown complex task utilising a combination of tactile, vision and action sensor data. To acquire these primitive actions, an approach known as Dynamic Motion Primitives (DMPs) will be used to imitate the bahaviour of a human’s action [1]. This one-shot learning approach will enable robot skills to be derived from observations of a human's solution to a task, omitting the requirement to analytically decompose and manually program a desired behaviour. The developed framework will combine such primitive actions in a hierarchical manner to accomplish tasks that require a more complex solution. On achieving this, we will further extend the approach to enable multiple robots to co-operate on a single task, utilising cognitive approaches for task allocation based on existing robot skills or a robot’s capability to perform a new skill. We will incorporate recent advances developed in the European project RoboHow [2], where previously learned primitive actions are obtained from a skills library residing in the Cloud, populated with primitive actions from many robot sources.

This project will make use of mobile robots that are available in the ISRC robotics lab [3], in particular the state of the art PR2 mobile manipulator robot [4], the Schunk manipulator arm robots [5], the BioTAC sensors and the Vicon system. The skills library will use a cloud architecture service such as Microsoft Azure [6] as a platform for robot knowledge sharing.

References

[1]Ijspeert, Auke Jan, Jun Nakanishi, and Stefan Schaal. "Movement imitation with nonlinear dynamical systems in humanoid robots." Proceedings, IEEE International Conference on Robotics and Automation, Vol. 2, 2002.

[2]http://www.robohow.eu

[3]http://isrc.ulster.ac.uk/Cognitive-Robotics-Team/Home.html

[4]http://www.willowgarage.com/pages/pr2/overview

[5]http://www.schunk.com/schunk_files/attachments/ModularRobotics_2010-06_EN.pdf

[6]http://www.windowsazure.com/en-us/home/scenarios/cloud-services/


Essential criteria

  • To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.
  • Experience using research methods or other approaches relevant to the subject domain

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • First Class Honours (1st) Degree
  • Masters at 70%
  • For VCRS Awards, Masters at 75%
  • Publications - peer-reviewed

Funding

    The University offers the following awards to support PhD study and applications are invited from UK, EU and overseas for the following levels of support:

    Vice Chancellors Research Studentship (VCRS)

    Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).

    This scholarship will cover full-time PhD tuition fees and provide the recipient with £15,000 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

    Vice-Chancellor’s Research Bursary (VCRB)

    Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).

    This scholarship will cover full-time PhD tuition fees and provide the recipient with £7,500 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

    Vice-Chancellor’s Research Fees Bursary (VCRFB)

    Fees only award (PhD fees + RTSG for 3 years).

    This scholarship will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

    Department for the Economy (DFE)

    The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 15,009 per annum for three years. EU applicants will only be eligible for the fee’s component of the studentship (no maintenance award is provided). For Non-EU nationals the candidate must be "settled" in the UK. This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

    Due consideration should be given to financing your studies; for further information on cost of living etc. please refer to: www.ulster.ac.uk/doctoralcollege/postgraduate-research/fees-and-funding/financing-your-studies


Other information


The Doctoral College at Ulster University


Reviews

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As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day

Adrian Johnston - PhD in Informatics

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Key dates

Submission deadline
Monday 19 February 2018

Interview Date
12 March 2018


Applying

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Contact supervisor

Professor Sonya Coleman


Other supervisors

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