Autonomous Sensing Platforms

From surveillance applications in controlled environments to navigating and mapping unknown hostile environments, autonomous, multi-sensor platforms are increasingly important to improve mission efficacy and reduce human risks.

Developing Object-centric Vision and Language Foundation Models for Robust Autonomous Sensing Platforms

Develop novel Deep Learning techniques to extend the ability of autonomous agents to track objects in visual scene in a more robust and generalisable way.

Differentiable physics engine for multi-robot loco-manipulation

This project seeks to implement a dImplementation of differentiable physics engine for distributed motion planning in multi-agent loco-manipulation tasks.

Leveraging model-based reinforcement learning for intervention and surveying in underwater environments

The aim of this research is to develop novel reinforcement learning algorithms that leverage modelling information.

Zero-shot coordination

The aim of this project is to enhance the coordination capabilities of autonomous agents during the deployment, either with other autonomous platforms or with human operators.

Exploring foundational models for decision making in autonomous robots

The central aim of this research is to investigate how foundation models can be effectively leveraged to enhance decision-making capabilities in single autonomous agents operating within complex, real-world environments.

Sensing system development for aviation

The aim of this PhD is the development of continuous embedded sensors that localize and detect impact damage in composite structures.

Loco-manipulation robot for surveillance in unstructured, hostile environment

Advancing loco-manipulation skills for robot surveillance in unstructured, dark, and hostile environments.

EPSRC and MoD CDT in Sensing, Processing, and AI for Defence and Security (SPADS)

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