Funded PhD Opportunity Kinaesthetic learning for robotic object manipulation
This opportunity is now closed.
Subject: Computer Science and Informatics
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.
 E. Kerr (2017) PhD dissertation, Ulster University.
 A. Gomez-Eguiluz (2018) PhD dissertation, Ulster University.
 Gillespie et al. (2017) Reinforcement learning for bio-inspired target seeking, Towards Autonomous Robotic Systems (TAROS), 19-21 July, University of Surrey.
 Siddique and Adeli (2013) Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing, John Wiley.
- Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC)
- Experience using research methods or other approaches relevant to the subject domain
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
Vice Chancellors Research Scholarships (VCRS)
The scholarships will cover tuition fees and a maintenance award of £15,009 per annum for three years (subject to satisfactory academic performance). Applications are invited from UK, European Union and overseas students.
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 fees component of the studentship (no maintenance award is provided). For Non EU nationals the candidate must be "settled" in the UK.
- Computing, Engineering and the Built Environment
- School of Computing, Engineering and Intelligent Systems
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