Bio-Inspired Models and Biologically Plausible Mechanisms for Long-Term Motion Learning

   Department of Computer Science

   Wednesday, May 01, 2024  Funded PhD Project (Students Worldwide)

About the Project

Biological systems can learn from interactions with their environment throughout their lifetime. Learning is a defining ability of biological systems, whereby experience leads to behavioral adaptations that improve performance. Artificial systems need the ability to learn on a continual basis to be successfully act and adapt in the real world. New generation of robotics & AI applications will require a new type of machine intelligence that is able to learn in a long-term manner. Such machines will need to acquire new skills without compromising old ones, adapt to changes, and apply previously learned knowledge to new tasks - all while conserving limited resources such as computing power, memory, and energy.

The ambition behind this project is to enhance the comprehension of biological underpinnings for long-term learning, including adaptability and autonomy exhibited by animals, as well as creating innovative machine intelligence and robotic solutions, and applied control systems characterized by greater adaptability, resilience, and energy efficiency. 

In this project, the successful PhD student will focus on one of the following areas: 

  • Modelling and transfer of human/animal excellence/knowledge for autonomous motion learning, detection & generation; 
  • Enhancement of sensory-motor and learning capabilities;
  • Optimized morphological design for behavioural variability;
  • Optimal dynamics control for motor control learning.

You will address these issues by: (1) modelling and designing control systems considering the bio-inspired models, and (2) analysing the systems using simulations, and experiments with various robotics systems available at the university.

The ideal applicant should possess experience and a keen interest in robotics, control, machine intelligence and signal processing. However, the specific focus of the research is likely to depend on the skills of the successful candidate. This is an exciting interdisciplinary research project that offers experience with design and control of a long-term learning intelligent machine. You will get opportunities to present your work at national and international conferences. The knowledge and skills you learn during this project will be applicable to systems from different fields such as human-robot collaboration & interactions, agriculture & aquaculture environment control, soft robotics & new actuations, rehabilitation, and embodied artificial intelligence. Furthermore, the successful candidate will have the privilege of being part of the Real-Time and Distributed Systems (RTDS) Research Group at the Department of Computer Science and will benefit from access to state-of-the-art high-performance computing and experimental facilities, as well as the opportunity to collaborate with renowned scholars and experts at York and beyond.

If you wish to discuss any details of the project informally, please contact Dr Pengcheng Liu, Real-Time and Distributed Systems (RTDS) Research Group, Email: .

Computer Science (8) Engineering (12)

Funding Notes

This project will be funded by an allocated DTP studentship. International applicants will be considered but the funding only covers UK fees.

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