About the Project
Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.
The PhD will be based in the School of Electrical and Mechanical Engineering and will be supervised by Dr Xin Zhang.
This project aims to develop an intelligent human–robot collaborative assembly framework that enables robots to understand and respond to human instructions through natural interaction. By integrating vision-language-action (VLA) models, the system interprets visual scenes, comprehends verbal or textual commands, and autonomously plans assembly actions, such as handover parts in real time. The research explores how VLA models (e.g., GPT-based models) can be combined with robot motion planning and control to achieve seamless collaboration between humans and robots in manufacturing or laboratory assembly tasks. The system aspires to enhance efficiency, adaptability, and safety in mixed human–robot work environments.
The work on this project will involve:
- Multimodal Understanding. Integrate vision and natural language processing to allow robots to perceive the workspace and interpret human instructions in natural language (e.g., “Pass me the screw” or “Assemble the left panel first”).
- Action Planning and Execution. Combine learned representations from VLA models with robot motion control algorithms, enabling context-aware and adaptive assembly actions without predefined scripts.
- Compliant control. Considering the physical interaction in the assembly task, the compliant control between robot with environments or humans will be achieved.
- Real Experiments. Design several feasible experiment scenarios, and test the developed VLA framework.
Project description
The next generation of intelligent manufacturing requires robots that can seamlessly collaborate with humans in complex and dynamic environments. This project focuses on developing a VLA–based human–robot collaboration system capable of understanding human intentions through natural communication and executing assembly tasks autonomously. The hardware system includes an ABB industrial robot and an assembly scenario, which has already been established at the robotics and automation lab in University of Portsmouth. Students will gain hands-on experience in robot programming, computer vision, and deep learning by our collaborative robotic platforms. Some related achievements can be found as follows:
- A practical PID variable stiffness control and its enhancement for compliant force-tracking interactions with unknown environments[J]. Science China Technological Sciences, 2023, 66(10): 2882-2896.
- An active-passive compliance strategy for robotic plugging and unplugging of rocket electrical connectors[J]. IEEE/ASME Transactions on Mechatronics, 2024, 30(2): 1014-1025.
General admissions criteria
You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in Robotics, Electrical Engineering, Mechatronics, Mechanical Engineering, Computer Science, or a related area. In exceptional cases, we may consider equivalent professional experience and/or Qualifications.
English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
International students will require a study visa from UKVI to pursue the degree in the UK. If the research is in a sensitive or technological subject, the student may also need to secure an Academic Technology Approval Scheme (ATAS) certificate from the UK Foreign Office.
Specific candidate requirements
The candidate can demonstrate their ability and aptitude for researching work, with a degree or transcripts of courses relevant to Mechanical Engineering and/or Computer Science. Programming and analytical skills (e.g., Python, ROS, control systems, or machine learning frameworks)
Any background and/or hands-on experience in robots is an advantage.
How to Apply
We’d encourage you to contact Dr Xin Zhang (xin.zhang@port.ac.uk) to discuss your interest before you apply, quoting the project code.
When you are ready to apply, please follow the 'Apply now' link on the Electronic Engineering PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.
When applying please quote project code: SEM10500526
Funding Notes
Funding Availability: Self-funded PhD students only
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only - eligibility criteria apply).
Bench fees
Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.
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