Cobots learning digital twins
Start date: July 2022
Expected end date: December 2025
This project seeks novel solutions to the following challenges: 1) How a robot gains skills and knowledge through biomimicry digital twining with minimal human intervention; 2) How a robot can adapt to varying operational conditions of the same task; 3) How a robot can apply the same learning ability to learn different tasks as a human does. The Project will investigate the theory and algorithms for task learning that will help retain the knowledge of skilled operators during autonomous or semi-autonomous operations, and robot digital twining the human via imaging system, force/tactile sensors and data fusion.
The PhD Researcher will work towards a cobot learning framework and methods that allow a collaborative robot to gain skills and knowledge via biomimicry learning digital twins, sensor data fusion and deep machine learning.
Supervisory Team
- Principal Supervisor: Professor Mats Isaksson
- Associate Supervisors: Professor John McCormick
- External Supervisor: Dr Fouad Sukkar
Research Outputs
- McCormick, John, Pyaraka, Jagannatha Chargee, Real Robot Dance Challenge: Exploring live and online dance challenge videos for robot movement, International Conference on Movement and Computing (MOCO 2024), Utrecht, Netherlands.
- McCormick, John, Pyaraka, Jagannatha Chargee, Choudray, Kartik, Track Back: A Human Robot Movement Installation Utilising Unity Digital Twin and Human Bio-mimicry, International Symposium on Electronic Art (ISEA 2024), Brisbane, Australia.
Associated Researchers