Lifelong robotic navigation using visual perception. Service robots are becoming a major part of our working and personal environments, in much the same way as personal computers already have. This project will develop new methods of practical and useful robot navigation that will enable Australia's industries and services to remain internationally competitive.
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
Representation and Reasoning for Cognitive Personal Robotics. Robotic systems are becoming increasingly more sophisticated and prevalent. Developing complex and maintainable robot programs to control these systems remains a significant challenge particularly given the diversity of robot platforms and application areas. This project aims to build on advances in problem solving and programming paradigms in Artificial Intelligence, applying them to learning sophisticated robot programs. These techn ....Representation and Reasoning for Cognitive Personal Robotics. Robotic systems are becoming increasingly more sophisticated and prevalent. Developing complex and maintainable robot programs to control these systems remains a significant challenge particularly given the diversity of robot platforms and application areas. This project aims to build on advances in problem solving and programming paradigms in Artificial Intelligence, applying them to learning sophisticated robot programs. These techniques have the potential to provide for elaboration tolerance, knowledge/program maintenance and optimisation of performance. This project aims to develop techniques for building sophisticated declarative robot programs. It aims to achieve this by learning procedural robot programs and turning them into maintainable declarative robot programs.Read moreRead less
Representing and reasoning about ability for robots to use the cloud. While robots have come a long way they are still hampered by processing and data storage limitations. Component based robot middleware and facilities provided by cloud computing provide means for addressing these issues. This project develops technology for representing and reasoning about robot abilities so as to take advantage of these advances.
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE160100090
Funder
Australian Research Council
Funding Amount
$250,000.00
Summary
Computational infrastructure for developing deep machine learning models. Computational infrastructure for developing deep machine learning models:
The computational infrastructure for developing deep machine learning models aims to enable new developments in machine learning of deep neural network models by providing the specialised computing necessary to train and evaluate the networks. In the last three years, deep networks have smashed previous performance ceilings for tasks such as object ....Computational infrastructure for developing deep machine learning models. Computational infrastructure for developing deep machine learning models:
The computational infrastructure for developing deep machine learning models aims to enable new developments in machine learning of deep neural network models by providing the specialised computing necessary to train and evaluate the networks. In the last three years, deep networks have smashed previous performance ceilings for tasks such as object recognition in images, speech recognition and automatic translation, bringing the prospect of machine intelligence closer than ever. Modern machine learning techniques have had huge impact in the last decade in fields such as robotics, computer vision and data analytics. The facility would enable Australian researchers to develop, learn and apply deep networks to problems of national importance in robotic vision and big data analytics. Read moreRead less
Structure-without-motion: large-scale 3D reconstruction from distributed and unorganised images. Vision-based 3D reconstruction is a frontier technology for a wide range of applications. This project will lead to novel 3D reconstruction methods and systems that are more efficient, more cost-effective and more accessible to ordinary user. The outcomes will directly contribute to National Research Priority Goal of smart information use.
Foundations of human-agent collaboration: situation-relevant information sharing. As automated systems become more sophisticated in their capabilities, the design of effective interaction with human operators becomes more demanding. Outcomes from this project will support the development of human-automation teams that can coordinate and collaborate in fast changing task environments.
Discovery Early Career Researcher Award - Grant ID: DE120100995
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Visual navigation for sunny summer days and stormy winter nights. This project will develop innovative techniques for camera-based navigation that recognise locations under a wide range of environmental conditions caused by day-night cycles, weather and seasonal change. These techniques will enable the widespread use of cheap and lightweight cameras in robot and personal navigation systems.
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
A computational theory of strategic deception. This artificial project aims to develop a theory of strategic deception and test it through an Artificial Intelligence model. The project will combine computational Theory-of-Mind concepts with recent scientific findings to allow us to better understand whether and how intelligent technologies of the future might deceive humans. The findings will provide new insights into how Artificial Intelligence technologies of the future will impact applied are ....A computational theory of strategic deception. This artificial project aims to develop a theory of strategic deception and test it through an Artificial Intelligence model. The project will combine computational Theory-of-Mind concepts with recent scientific findings to allow us to better understand whether and how intelligent technologies of the future might deceive humans. The findings will provide new insights into how Artificial Intelligence technologies of the future will impact applied areas of computing, where simulating advanced forms of social behaviour and cognition, including deception, will become increasingly significant.Read moreRead less