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Social Human-Robot interaction using visual object recognition and hand pose estimation

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  • Full or part time
    Dr A Galata
    Prof A Cangelosi
  • Application Deadline
    Applications accepted all year round

Project Description

Recent advances in Machine Learning (Deep learning), Robotics and Computer vision have naturally led to an interest in techniques that enable Human-Robot interaction within a social setting. Such techniques can enable robots to become helpers, guides and experienced companions to humans during different tasks or activities. For example robots can help with the rehabilitation process of stroke patients in a medical setting, or guide young children in problem solving educational tasks.

This project is interested in developing Computer Vision techniques that enable robots to learn to interact with humans by observing the way in which humans interact with other humans and/or objects in their environment. Visual hand tracking, gesture recognition and hand-object pose estimation in the context of human-robot interaction are areas in which we expect potential PhD students will make their original contributions.

A strong scientific background together with excellent problem solving and programming skills are essential. An interest in visual learning using machine learning techniques is desirable.

The project will be jointly supervised by Dr Aphrodite Galata and Prof Angelo Cangelosi.

Funding Notes

This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. The funding is available to citizens of a number of European countries (including the UK). In most cases this will include all EU nationals. However full funding may not be available to all applicants and you should read the full department and project details for further information.

How good is research at The University of Manchester in Computer Science and Informatics?

FTE Category A staff submitted: 44.86

Research output data provided by the Research Excellence Framework (REF)

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