PhD Study : Kinaesthetic learning for robotic object manipulation

Summary

Tactile sensing is an emerging area in robotics with applications to object recognition, material identification, and grasp control [1,2]. However, Kinaesthetic learning (also known as tactile learning) integrating tactile and force sensing to perform physical movements of objects or interact with unknown environments is still widely under-researched. Despite the fact that object manipulation comes as second nature to humans, robotic manipulation has been limited by the lack of tactile information feedback, which is indispensable to achieve human-level manipulation skills.

The goal of this project is to enhance robot-object interaction and manipulation through experience that involves the combination of force and tactile information (kinaesthetics). Nowadays robots make strong implicit assumptions about the objects in their surroundings, i.e. objects are fixed (like walls and furniture), or can be manipulated (like glasses, trays, and mugs). This approach is clearly restrictive – as it does not scale to the manipulation of unknown objects – and troublesome – as the robot might be damaged trying to manipulate the wrong object. Grounded on the Cognitive Robotics group expertise in tactile sensing [1,2] and learning [3,4] this project will investigate ways to apply neural and reinforcement learning to tactile information for a robot to perform manipulation tasks of different objects.

This project aims at overcoming these implicit assumptions about the world, endowing robots with the ability to learn these concepts through real world experimentation. Therefore, the robot will learn different tactile and physical properties of the object through experimentally manipulating them. This project will enhance robotic manipulation autonomy and has multiple potential applications ranging from industrial to assistive robotics.

[1] E. Kerr (2017) PhD dissertation, Ulster University.

[2] A. Gomez-Eguiluz (2018) PhD dissertation, Ulster University.

[3] Gillespie et al. (2017) Reinforcement learning for bio-inspired target seeking, Towards Autonomous Robotic Systems (TAROS), 19-21 July, University of Surrey.

[4] Siddique and Adeli (2013) Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing, John Wiley.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• 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 and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

The following scholarship options are available to applicants worldwide:

• Full Award: (full-time tuition fees + £19,000 (tbc))
• Part Award: (full-time tuition fees + £9,500)
• Fees Only Award: (full-time tuition fees)

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) 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.

• Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
• Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
• Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
• Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living