This dissertation presents temporal-difference learning methods for building world models that enable intelligent agents and robots to predict, plan and act. It introduces TD-MPC, which learns compact latent dynamics, rewards and value estimates for model-predictive control, and TD-MPC2, a more robust successor requiring little task-specific tuning. Experiments demonstrate faster real-world robot learning, scalable multitask performance and a unified model spanning 200 tasks across ten domains. The research also examines interactive generative world models and their tendency to hallucinate physically implausible futures. Targeted data collection in uncertain regions substantially reduces these errors, suggesting routes toward safer, uncertainty-aware autonomous systems and robotics.

This research uses AI-powered markerless motion capture to preserve Indigenous cultural dances as digital archives. By recording thousands of movement data points, it safeguards intangible cultural heritage for future generations. The work aims to extend this technology globally, ensuring every culture has the tools to preserve its unique traditions.

This research develops a fast algorithm to identify rare gravitational lenses in images from the Euclid Space Telescope. By analysing how galaxies bend light, it maps the distribution of invisible dark matter, helping astronomers investigate one of the greatest mysteries in physics: the nature and composition of dark matter.

This research develops a brain-inspired optical imaging system that mimics human vision to reconstruct objects hidden by fog, smoke, and biological tissue. Combining event-based cameras, spiking neural networks, and neuromorphic processors, it enables fast, energy-efficient imaging with applications in autonomous vehicles, emergency response, and non-invasive medical diagnostics.

This research develops Roblonski, a compact robotic platform that automates photoredox chemistry using microscopic droplets and visible light. By reducing chemical use, waste, and manual effort by over 90%, it generates high-quality data for AI-driven discovery, paving the way for faster, greener, and more intelligent self-driving chemistry laboratories.

This research investigates how the brain makes decisions under uncertainty by studying mice navigating reward-based mazes. Rather than relying on memorisation, mice continually update mental models through active exploration. These findings improve our understanding of anxiety disorders and may inspire more adaptive artificial intelligence systems.

This research teaches AI to understand and generate the sense of touch by combining visual information with high-resolution tactile data. The technology enables realistic digital textures, improves online shopping, enhances virtual experiences, and creates accessible tactile graphics for blind and low-vision users, making AI more inclusive and human-centred.

This research develops microscopic copper wire "bridges" that improve heat transfer between computer chips and cooling systems. By reducing chip temperatures by around 3°C, the technology can lower data centre cooling energy by approximately 10%, improving efficiency and supporting more sustainable AI infrastructure.

This research develops scalable motion-planning algorithms that enable large teams of robots to work together safely and efficiently. By combining machine learning with search algorithms, the work delivers both speed and reliability, supporting applications from automated warehouses to disaster response, infrastructure repair, and future space exploration.

This research has developed an electronic nose that combines gas sensors with machine learning to detect food spoilage and hidden allergens. By recognizing unique scent signatures more accurately than the human nose, the technology could improve food safety, prevent allergic reactions, reduce food waste, and eventually be integrated into everyday devices.