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  Building Baymax - Flexible Robot Skin: Smart sensors and algorithms

   School of Physics, Engineering and Technology

  ,  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

In this project, we aim to develop the next generation of household robots (not vacuum cleaners but multi-task robotic manipulators) that are safe to interact with, even for children and the elderly. Several lightweight robots have been developed especially for this purpose: with external force detection and emergency stops. However, the detection is often on the joint torque level which makes it difficult to locate the point of contact, thus the reaction time is typically long. In contrast, humans can locate and react to forces all over the body rapidly. The main difference is the largest organ in the human body - the Skin. The skin is a soft structure that contains widespread multi-modal sensors that can process not only force and pressure but also heat. We aim to endow such capability to robots by implementing a flexible skin based on MEMS technology that is equivalent to human skin.

MEMS technology is ubiquitous in the digital world - there are likely 10-20 MEMS sensors in any single smartphone we own. MEMS technology offers distinct advantages such as small form factors, low cost, and negligible power consumption. We have witnessed the recent emergence of “flexible MEMS”, whereby MEMS sensors are now implemented on flexible substrates, making them ideal for realising the intelligent skin for the robots. To achieve flexible intelligent skin, you will investigate multiple aspects within the highly promising field of flexible MEMS, including but not limited to advanced materials (e.g., flexible substrates and sensor materials), microfabrication (i.e., making the sensors using the materials), and novel sensor designs (e.g., how to make multi-modal sensors that can detect force, pressure and temperature simultaneously).

Algorithms to process the sensory data and subsequently derive policy for robot motion are equally important. To process the sensory data and react, you will investigate various models to learn the multimodal data representation so as to derive motion policy for the manipulation of soft/fragile objects (similar to developing the brain of the robot).

To achieve this flexible skin, you will be supported to investigate either or both of the two aspects: (1) multi-modal micro-scale sensors based on flexible substrates, and (2) robotics manipulation based on multimodal sensory feedback. You will be jointly supervised by Dr. Chun Zhao (sensors) and Dr. Jihong Zhu (robotics). The supervisors have strong connections with both academia (e.g., University of Cambridge, TU Delft, KU Leuven, CNRS) and industry (e.g., Honda Research Institute Europe). The supervisors encourage collaboration and academic exchanges, and you will have many opportunities for national and international collaboration.

You will have access to the well-equipped laboratories available at the University of York, and will gain comprehensive training and experience in a wide range of tasks essential to the project, e.g., novel tactile sensor materials, design and fabrication, and learning-based robotics manipulation. Full-time training for postgraduate students, tailored to your particular degree background, is provided within the active research groups.

Entry requirements:

Candidates should have (or expect to obtain) a minimum of a UK upper second class honours degree (2.1) or equivalent in Electrical, Electronic, Control, Mechanical Engineering and Computer Science or a closely related subject. It is preferred that candidates hold a Master’s degree in the abovementioned areas. Applications will be considered on a competitive basis with regard to the candidate’s qualifications, skills, experience and interests.

How to apply:

Applicants should apply via the University’s online application system at Please read the application guidance first so that you understand the various steps in the application process.

Engineering (12)

Funding Notes

This is a self-funded project and you will need to have sufficient funds in place (eg from scholarships, personal funds and/or other sources) to cover the tuition fees and living expenses for the duration of the research degree programme. Please check the School of Physics, Engineering and Technology website View Website for details about funding opportunities at York.

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