Funding

Self-funded

Project code

SEM10380526

Start dates

October, February and April

Application deadline

Applications accepted all year round

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.

 

The work on this project will involve:

  • SRA Optimization. Develop a wearable comfortable backpack that can install the small-sized robot on human torso with an adjustable cross-workspace between the SRA and human users. Develop a simulation to model the interaction scenarios between the SRA and the human.
  • Wearable interface. Integrate several wearable sensors to obtain and estimate the human motion state and the external environment.
  • Multi-Sensor-LLM-Action (MSLA) framework. Leveraging the large language model (LLM), allow the SRA to perform task-level decision-making while ensuring safe and intuitive operation alongside human partners.

This project aims to develop a supernumerary robotic arm (SRA)—an artificial third arm to augment human manipulation capabilities and address tasks that cannot be effectively handled by the natural limbs alone.
This system includes a wearable interface that integrates multiple sensors, such as inertial measurement units (IMUs), electromyography (EMG), microphones, and cameras. These sensing modalities will be embedded into the SRA and interfaced with large language models (LLMs) to enable seamless interaction and collaboration between the robotic arm and human users.

Supernumerary Robotic Arm (SRA) is a hot topic in the field of robotics. It represents a step toward symbiotic robotics, where humans and machines work in seamless coordination. Meanwhile, this aligns with broader trends in cognitive augmentation and embodied AI. Students will gain hands-on experience in robot programming, computer vision, and deep learning, using collaborative robotic platforms. The Robotics and Automation lab will support this project with hardware. The hardware system has already been established, and some related achievements can be found as follows:

  1. Bi-directional human-robot handover using a novel supernumerary robotic system[C]//2023 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO). IEEE, 2023: 153-158.
  2. A human motion compensation framework for a supernumerary robotic arm[C]//2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids). IEEE, 2023: 1-8.

An Agile Large-Workspace Teleoperation Interface Based on Human Arm Motion and Force Estimation[C]//2024 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2024: 117-122.

 

Fees and funding

Visit the research subject area page for fees and funding information for this project.

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).

 

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.

Entry requirements

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.

 

 

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 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.

If you want to be considered for this self-funded PhD opportunity you must quote project code SEM10380526 when applying.