Imitation Learning for Robotic Manufacturing Transformation

GTA funded PhD studentship in Computing

Highlights

1. Advance State-of-the-Art in Imitation Learning: Elevate existing imitation learning methodologies (such as GAIL, Q-learning and Meta-learning) to enhance robotic capabilities in complex manufacturing processes.
2. Develop a Robotic Imitation Learning Framework for Manufacturing: Create a pioneering imitation learning framework tailored for robotic applications in manufacturing operations, including precision tasks like welding and machining.
3. Curate a Digital Manufacturing Dataset: Support the curation of a vast digital dataset derived from the DigitalMetal consortium's findings, aimed at supporting and advancing robotic manufacturing research.

Project

Robotic technology plays a pivotal role in the UK's digital strategy, enhancing the digital and data roadmap for the transition from traditional manufacturing. This shift aims to boost the sector's competitiveness, sustainability, and innovation.

Despite advances, experienced engineers remain crucial in manufacturing processes like welding and machining. Our goal is to equip robots with the ability to learn from human manufacturing experts, enabling them to perform complex tasks intelligently. While there have been successes in applying artificial intelligence, such as imitation learning, to teach robots, challenges remain in refining learning approaches (from data collection to learning algorithms) and improving the performance accuracy and robustness of autonomous robotic operations.

Project Objectives:

This project aims to address these challenges by:

• Modelling the robotic manufacturing process and applying techniques like domain randomisation and adaptation to narrow the gap between simulated environments and real-world applications.
• Collaborating with relevant EPSRC funded projects to organize and collect manufacturing data.
• Developing few-shot learning or meta-learning approaches to enhance the practicality of imitation learning.
• Advancing state-of-the-art imitation learning methods, such as Inverse Reinforcement Learning (IRL) and Generative Adversarial Imitation Learning (GAIL), and exploring integrated approaches with reinforcement learning for processes like welding.

 Requirements for Candidates:

In addition to meeting the University's PhD degree entry requirements, potential candidates should possess a relevant degree and/or experience in robotics and artificial intelligence. Preference will be given to applicants with research experience in imitation learning or reinforcement learning and familiarity with the Robot Operating System (ROS).

Opportunities for the Successful Candidate:

The selected candidate will have access to facilities including the Universal Robotics UR5e arm, 3D printers, high-performance computing, and more. They will join the University of Leicester's extensive digital metal and robotics network, benefiting from additional resources and support. The project will be jointly supervised by the School of Computing and Mathematical Sciences and the School of Engineering. The candidate may access the data resources from the UK’s DigitalMetal Consortium.

Enquiries to project supervisor Dr Daniel Z. Hao [Email Address Removed]   or [Email Address Removed]

Further details and application advice at https://le.ac.uk/study/research-degrees/funded-opportunities/cms-gta