I'm a PhD student studying Reinforcement Learning for Robot Navigation at Cardiff University.
My research interests include:
End-to-End Deep RL
Robotics Foundation Models
Imitation Learning
Publications
Prior research demonstrated the effectiveness of end-to-end reinforcement learning for PointGoal navigation tasks within indoor environments. Given 2.5 billion frames of experience, a navigation policy can be trained to achieve a success rate of 0.94 when deployed in unseen environments.
A limitation of this approach is its reliance on perfect localisation, which is unrealistic for real-world deployment scenarios where localisation must be estimated, inevitably introducing errors.
This work studies the effectiveness of integrating a traditional vision-based SLAM algorithm with a reinforcement learning-based PointGoal navigation policy.
We demonstrate how fine-tuning a pretrained navigation policy on realistic localisation estimates can increase the success rate by 14% (0.71 → 0.85) and SPL by 15% (0.66 → 0.81) when compared to deploying policies in a zero-shot manner.
This project applied deep learning methods to the problem of diagnosing faults within electrical machinery.
Used a transfer learning approach to detect rotor and bearing faults within an induction motor.
A pretrained ResNet-34 was used to extract features from infrared images, and vibration signals were encoded using the Gramian Angular Fields algorithm.
The best performing model achieved a mean test accuracy of 98%.
Projects
Teaching
Robotics & Image Processing - Lead Lecturer (2022/23)
Introduction to C++ - Teaching Assistant (2023/24)
Engineering Optimisation with Python - Teaching Assistant (2023/24)