Honours and Masters project

This project addresses the language ignoring problem in embodied AI, where robots learn visual shortcuts instead of following instructions. Building on our preprint establishing the relationship between planning width (instruction granularity) and learning difficulty, you will develop WALR—a reward design framework that adapts to instruction complexity. WALR scales language grounding rewards based on instruction granularity (coarse vs.

This project develops PACE-Drone, an intelligent drone planning system that learns from experience rather than following pre-programmed scripts. Unlike current drones that treat each mission independently, PACE-Drone maintains a persistent belief over user preferences via Bayesian learning, actively discovers implicit constraints from historical mission logs, and balances exploration with task completion based on instruction granularity.

Our research explores novel map representations and projections.

This project seeks to design and trial new map representations for seeing Australian population data sets in new and ideally more effective ways. 

Why is this needed? 

DeFi (Decentralised Finance) [Link] provides financial instruments and services through smart contracts on block chain systems. Comparing to traditional finance systems, DeFi eliminates the need of brokerages, exchanges, or banks as intermediates, and allows users to perform various financial activities including lend/borrow funds, trade cryptocurrencies, and earn interests. 

The research project aims to build:

- An Energy Market Simulator for Future Energy Systems (Smart Grid). 

Future energy systems are envisioned to be running decentrally with full automatic control, high proportion of renewable energy (e.g., wind & solar), and abundant storage facilities. With many types of renewable energy sources are weather and climate dependent, accurate simulators with good visualization and data analytic capabilities are needed for operators to control the grid.

This project focuses on understanding and applying control charts as a tool for monitoring and improving process quality. It involves designing some basic control charts and evaluating their performance in detecting process variations under different conditions. The evaluation will be based on key metrics such as Average Run Length (ARL), false alarm rate, and detection speed, providing insights into the effectiveness of various chart types in maintaining quality standards.

Passive acoustic monitoring is a well-established, non-invasive technique for wildlife monitoring, with growing interest in bioacoustic individual-level recognition—the ability to distinguish individual animals based on their vocalisations. While existing approaches perform well when all individuals are known a priori, wildlife populations are inherently dynamic, making such closed-set assumptions unrealistic in natural environments.

This project aims to design and implement an Agentic AI for Human Assistance (the “assistant agent”) to support everyday information work, e.g. email coordination. Unlike conventional chatbots, the proposed system follows an agentic workflow: it can plan, use tools, and execute multi-step tasks, while incorporating Human-in-the-Loop (HITL) mechanism at critical decision points to ensure trustful, safety, controllability, and explainability.

Introduction

Rare neurodegenerative diseases, including the hereditary cerebellar ataxias, pose significant challenges for disease monitoring. Small patient cohorts, heterogeneous progression patterns, and slow rates of progression make it difficult to track disease change using conventional biomarkers. Although clinical rating scales remain the standard for assessing severity, they are subjective, prone to measurement noise, and often lack sensitivity to subtle longitudinal decline.