Robots have demonstrated their superiority in increasing productivity, efficiency, and consistency across various industries, especially in factory settings. However, how to integrate robot precision with human adaptability and cognition to create a seamless and efficient collaborative system is still an open issue. This PhD aims to develop bio-informed physical human-robot interaction (pHRI) algorithms that enable robots to intuitively adapt to human intentions and effectively learn from human demonstrations.
The successful candidate will investigate methods for understanding and predicting human motion intentions, and construct skill representations that allow robots to learn and generalize these skills across similar tasks by incorporating kinematic measures and physiological measures such as surface electromyography (sEMG) and functional near-infrared spectroscopy (fNIRS). Musculoskeletal models and fNIRS-based approaches will be explored to estimate muscular and cognitive load during pHRI. These models will be used to generate feed-forward information, enabling robots to optimize the physical and cognitive burden on human operators. The candidate will test the developed technologies in exemplary scenarios, such as collaborative puncturing and cutting, to evaluate their efficacy in collaborative manufacturing settings.
Applicants should have or expect to achieve at least a UK 2.1 honours degree or its international equivalent) in Control Engineering, Computer Science, Mechatronic Engineering or related disciplines. Experience in robotic manipulation, human biomechanics modelling, and human-robot interaction will be an advantage.
The duration of the PhD is 3.5 years and the proposed starting date is September.
Funding is available to cover the tuition fees, as well as a stipend at standard UKRI rate for up to 3.5 year (for 23/24 this is expected to be £18,022 per annum).
You will need to submit an online application through our website here: https://uom.link/pgr-apply
When you apply, you will be asked to upload the following supporting documents:
• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• You will be asked to supply contact details for two referees on the application form
• English Language certificate
· Supporting statement
We strongly recommend that you contact the supervisor to discuss the application before you apply. The email address for Dr Zhenhong Li is [Email Address Removed].
The following EDI statement must be included in all advertised projects:
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.
We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).
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