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PhD Studentship: Lifelong learning with robotic vacuum cleaners in social spaces

Computer Science

Location:  UK Other
Closing Date:  Monday 30 January 2023
Reference:  SCI2116

Although domestic service robots are successfully embedded with intelligent perception, planning, and scheduling abilities to help us with our household chores, failures and navigation errors are still very common, and there is little adaptation to recurring errors in state-of the-art systems.

Such errors pose a severe limitation on the long-term autonomy since they may cause a breakdown of the whole robotic operation. Hence, the ability to detect and recover from errors during navigation is an essential ability for an autonomous service robot that can run for extended periods of time. In addition, functioning in human settings, these robots should be programmed to adhere to social cues in a context-dependent manner, not only to enable safe, but also acceptable functionality. This PhD project will focus on these challenges by targeting multiple strands of research in perception, planning, human-in-the-loop learning, and shared control for service robots.

Funding and Programme Details: 

The studentship is co-funded by Beko Plc. (

The student will benefit from placement opportunities and closely work with the company’s research division. 

The studentship is fully funded and includes the home (UK) tuition fees plus full stipend, tax-free at the RCUK rate (minimum £17,668 per annum).

Entry Requirements:

Applicants should have, or expected to achieve, at least a first-class or upper second-class BSc or MSc degree (or equivalent) in Computer Science, Engineering, or a related subject. Applicants with significant relevant non-academic experience are also encouraged to apply.

If English is not the candidate’s first language, they must provide evidence before the beginning of the studentship that they meet the University minimum English Language requirements (IELTS 6.0 with at least 5.5 in each element).

Applicants should have an excellent background in software engineering, and should be committed to applying their research to real robotic systems interacting with people in challenging environments. Familiarity with machine learning, and hands on experience with robotics hardware as well as relevant tools and software for robotics is a plus.

Application Process:

Please send an Expression of interest to in PDF format, consisting of 

1) your CV and relevant links (Github, website etc.)

2) a cover letter

3) your transcript

Post interview, application should be made through the MyNottingham system stating the supervisor name and project title.

Informal inquiries about the post can be made to Dr Ayse Kucukyilmaz at

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