Human–Robot Interaction via Sensorimotor Integration Between Wearable Interface and Supernumerary Robotic Arm

About the project

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 and Prof. Zhaojie Ju . 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. The work on this project will: 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. Project description 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: 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. 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. 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. 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 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: SEM10380526.

Research areas