Visual Simultaneous Localisation and Mapping in Deformable Environments. This project aims to investigate the problem of building a three-dimensional map of a deformable environment in real-time using images and at the same time localising the camera within the map. This project expects to generate new knowledge in the area of simultaneous localisation and mapping in deformable environments using visual sensors. Expected outcomes include in-depth understanding of the fundamental sensing requirem ....Visual Simultaneous Localisation and Mapping in Deformable Environments. This project aims to investigate the problem of building a three-dimensional map of a deformable environment in real-time using images and at the same time localising the camera within the map. This project expects to generate new knowledge in the area of simultaneous localisation and mapping in deformable environments using visual sensors. Expected outcomes include in-depth understanding of the fundamental sensing requirements for the problem to be solvable, the achievable accuracy, and efficient algorithms for achieving accurate three-dimensional reconstruction of deformable environments. The research outcomes from this project offer significant benefits to diverse areas such as minimally invasive robotic surgery.Read moreRead less
Robotic Perception with Unconventional Sensors . Autonomy in robotic systems currently relies on conventional sensors such as lasers and cameras. Alternative sensing modalities as in the case of active electromagnetic sensors are commonly used to detect flaws, cracks and assess infrastructure’s integrity, however, fundamental research questions preclude their use for robotic perception. This project will develop the theory and algorithms to enable perception tasks such as localisation, mapping a ....Robotic Perception with Unconventional Sensors . Autonomy in robotic systems currently relies on conventional sensors such as lasers and cameras. Alternative sensing modalities as in the case of active electromagnetic sensors are commonly used to detect flaws, cracks and assess infrastructure’s integrity, however, fundamental research questions preclude their use for robotic perception. This project will develop the theory and algorithms to enable perception tasks such as localisation, mapping and recognition with unconventional sensors. The outcomes of this research have the potential to improve the effectiveness of critical civil infrastructure maintenance technology through accurate and reliable inspections, and the reduced need for human intervention.Read moreRead less
Unifying Foundations for Intelligent Agents. This project aims to drive forward the development of rigorous foundations for intelligent agents. The agent framework, the expected utility principle, sequential decision theory, and the information-theoretic foundations of inductive reasoning and machine learning have already brought significant order into the previously heterogeneous scattered field of artificial intelligence. This project aims to investigate an information-theoretic approach towar ....Unifying Foundations for Intelligent Agents. This project aims to drive forward the development of rigorous foundations for intelligent agents. The agent framework, the expected utility principle, sequential decision theory, and the information-theoretic foundations of inductive reasoning and machine learning have already brought significant order into the previously heterogeneous scattered field of artificial intelligence. This project aims to investigate an information-theoretic approach towards a unifying foundation for intelligent agents, which has recently spawned impressive applications. The theory is expected to provide a gold standard and valuable guidance for researchers working on smart software.Read moreRead less
Learning Robotic Navigation and Interaction from Object-based Semantic Maps. Our project aims to develop new learning algorithms that enable robots to perform high-complexity tasks that are currently impossible. Compared to existing methods that rely on low-level sensor data, we aim to achieve this by learning from a high-level graph representation of the environment that captures semantics, affordances, and geometry. The outcome would be robots capable of using human instructions to efficiently ....Learning Robotic Navigation and Interaction from Object-based Semantic Maps. Our project aims to develop new learning algorithms that enable robots to perform high-complexity tasks that are currently impossible. Compared to existing methods that rely on low-level sensor data, we aim to achieve this by learning from a high-level graph representation of the environment that captures semantics, affordances, and geometry. The outcome would be robots capable of using human instructions to efficiently learn complex interaction and navigation behaviours that transfer to unseen environments. Our research should benefit new applications in domains of economic and societal importance that are currently too complex, unsafe, and uncertain for robot assistants, such as aged care, advanced manufacturing and domestic robotics.Read moreRead less
Active Visual Navigation in an Unexplored Environment. This project will develop a new method for robotic navigation in which goals can be specified at a much higher level of abstraction than has previously been possible. This will be achieved using deep learning to make informed predictions about a scene layout, and navigating as an active observer in which the predictions informs actions. The outcome will be robotic agents capable of effective and efficient navigation and operation in previous ....Active Visual Navigation in an Unexplored Environment. This project will develop a new method for robotic navigation in which goals can be specified at a much higher level of abstraction than has previously been possible. This will be achieved using deep learning to make informed predictions about a scene layout, and navigating as an active observer in which the predictions informs actions. The outcome will be robotic agents capable of effective and efficient navigation and operation in previously unseen environments, and the ability to control such agents with more human-like instructions. Such capabilities are desirable, and in some cases essential, for autonomous robots in a variety of important application areas including automated warehousing and high-level control of autonomous vehicles. Read moreRead less
Cybersecurity ethics training simulations for values-based decision-making. This Project will investigate ways to train reflective ethical decision making in cybersecurity management through the design of interactive social simulations. The Project will advance understanding and management of human factors in cybersecurity breaches and the field of serious game design for cybersecurity training by using new techniques for building artificially intelligent virtual agents, drawing on interdiscipli ....Cybersecurity ethics training simulations for values-based decision-making. This Project will investigate ways to train reflective ethical decision making in cybersecurity management through the design of interactive social simulations. The Project will advance understanding and management of human factors in cybersecurity breaches and the field of serious game design for cybersecurity training by using new techniques for building artificially intelligent virtual agents, drawing on interdisciplinary expertise in ethics, artificial intelligence and serious game design. Expected outcomes of the Project include a new framework and technologies for cybersecurity training. This should provide significant benefits through deeper understanding of the ethical impact of new cybertechnologies and training solutions.Read moreRead less
Intelligent pattern recognition of water end uses enabling recommendations. This project aims to develop a hybrid machine learning method for autonomously disaggregating high- and low-resolution water flow data received from smart meters into discrete end-use events, and a customised recommender system for efficient resource demand management. Project novelty and significance relates to this coupling and autonomous disaggregation of datasets from advanced sensors, enabling more efficient utility ....Intelligent pattern recognition of water end uses enabling recommendations. This project aims to develop a hybrid machine learning method for autonomously disaggregating high- and low-resolution water flow data received from smart meters into discrete end-use events, and a customised recommender system for efficient resource demand management. Project novelty and significance relates to this coupling and autonomous disaggregation of datasets from advanced sensors, enabling more efficient utility services delivery and lower customer utility bills. Project benefits include enabling utilities to better manage and plan resources in the information age, while empowering customers with real-time water end-use data and behaviour changing consumption recommendations.Read moreRead less
Explanation in artificial intelligence: a human-centred approach. This project aims to produce validated methods for creating human-centred explanations of decisions made by artificial intelligence (AI). Trial deployment of AI devices has resulted in the requirement for explanations of how AI makes decisions, where developed AI systems gave insufficient consideration of how decision logic would be explained to people. This project positions 'explainable AI' within the intersection of human-compu ....Explanation in artificial intelligence: a human-centred approach. This project aims to produce validated methods for creating human-centred explanations of decisions made by artificial intelligence (AI). Trial deployment of AI devices has resulted in the requirement for explanations of how AI makes decisions, where developed AI systems gave insufficient consideration of how decision logic would be explained to people. This project positions 'explainable AI' within the intersection of human-computer interaction, computer science and cognitive psychology. The expected outcomes of this project are new methods, models and algorithms for explaining different types of AI models to people. This project should result in improved understanding and trust of decisions made by AI systems, mitigating some societal concerns about AI-based decision making.Read moreRead less
Self-organised communication as a foundation of large, complex societies. This Project aims to investigate how evolution has shaped the self-organisation of robust communication networks that emerge in large animal collectives from the actions of individuals following only simple, local rules. It expects to generate new knowledge into the fundamental principles guiding the self-organisation of networks that can sustain a complex society. Empirical work with ant colonies will inform the construct ....Self-organised communication as a foundation of large, complex societies. This Project aims to investigate how evolution has shaped the self-organisation of robust communication networks that emerge in large animal collectives from the actions of individuals following only simple, local rules. It expects to generate new knowledge into the fundamental principles guiding the self-organisation of networks that can sustain a complex society. Empirical work with ant colonies will inform the construction of simulation models to push the investigation beyond experimental limits. The Project should significantly advance our understanding of how communication networks enable the development of large societies, and thus of how to better manage autonomous man-made networks, most importantly the Internet-of-Things.Read moreRead less
AI Planning: The Next Generation. This is a project in Artificial Intelligence. It aims at extending and integrating automated planning (and other forms of reasoning) with learning to produce a new generation of planning systems that are robust, safe, scalable, and trusted. These are some of the most significant issues to address to accelerate the adoption of planning systems in industry. Expected outcomes include a pipeline to learn rich symbolic planning models from narrated demonstration vide ....AI Planning: The Next Generation. This is a project in Artificial Intelligence. It aims at extending and integrating automated planning (and other forms of reasoning) with learning to produce a new generation of planning systems that are robust, safe, scalable, and trusted. These are some of the most significant issues to address to accelerate the adoption of planning systems in industry. Expected outcomes include a pipeline to learn rich symbolic planning models from narrated demonstration videos, new ways to represent, learn, and search for generalised policies that are scalable and robust, and approaches to verify and explain generalised policies. The new systems should benefit the aerospace industry by assisting humans in assembling and delivering aerospace products.Read moreRead less