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
Mid-Career Industry Fellowships - Grant ID: IM230100042
Funder
Australian Research Council
Funding Amount
$980,358.00
Summary
Unlocking the full reproductive potential for hybrid wheat breeding. Globally, wheat is cultivated as an inbred self-fertile crop with yield gains stagnating over the last decades. This contrasts with unabated yield gains and yield stability achieved for rice and corn through hybrid breeding and cross-pollination. Wheat hybrids hold potential for a 10-22% yield boost, but commercial deployment is restricted due to high seed production costs, a result of wheat’s floral architecture and poor outcr ....Unlocking the full reproductive potential for hybrid wheat breeding. Globally, wheat is cultivated as an inbred self-fertile crop with yield gains stagnating over the last decades. This contrasts with unabated yield gains and yield stability achieved for rice and corn through hybrid breeding and cross-pollination. Wheat hybrids hold potential for a 10-22% yield boost, but commercial deployment is restricted due to high seed production costs, a result of wheat’s floral architecture and poor outcrossing characteristics. This project aims to reduce costs by improving wheat’s female receptivity to airborne pollen, a major bottleneck to commercial realization of hybrids globally. Higher and more stable yields from wheat hybrids will ensure food security in the face of climate uncertainty and growing population.Read moreRead less
The Impact Of The Neonatal Gut Microbiome On Specific And Nonspecific Vaccine Responses.
Funder
National Health and Medical Research Council
Funding Amount
$661,496.00
Summary
Humans are colonised by a large and diverse group of microorganisms, collectively known as the microbiome. The gut microbiome, in particular, hosts an enormous abundance and diversity of bacteria, which perform a range of essential beneficial functions. Our study will investigate whether disruption of the gut microbiome in newborns, for example through antibiotic usage or maternal diet, leads to an impairment of subsequent immune responses to childhood immunisations.
The development of vaccines and better treatments for HIV-AIDS and Hepatitis C are urgent global health priorities. This Program will undertake studies to better understand effective immunity against HIV and hepatitis C, allowing the rational design and testing of novel vaccines and treatments. The Program brings together a team of researchers with skills in basic virology and immunology with those providing expertise in translating findings in the laboratory into human clinical trials.
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
I am a molecular virologist researching the host response to hepatitis C virus (HCV) infection with the aim of understanding how the liver clears HCV infection. An understanding of this process will hopefully lead to novel antiviral strategies to combat not only HCV but a broad range of other viral infections.
ARC Centre of Excellence for Robotic Vision. Robots are vital to Australia's future prosperity in the face of high relative wages, low or decreasing productivity, and impending labour shortages. However the work and workplaces of our most important industries are unstructured and changeable and current robots are challenged by their inability to quickly, safely and reliably "see" and "understand" what is around them. The Centre's research will create the fundamental science and technologies th ....ARC Centre of Excellence for Robotic Vision. Robots are vital to Australia's future prosperity in the face of high relative wages, low or decreasing productivity, and impending labour shortages. However the work and workplaces of our most important industries are unstructured and changeable and current robots are challenged by their inability to quickly, safely and reliably "see" and "understand" what is around them. The Centre's research will create the fundamental science and technologies that will allow robots to see as we do, and overcome the last barrier to the ubiquitous deployment of robots into society for the benefit of all.Read moreRead less
Hepatitis C affects a quarter of a million Australians, causing insidious but progressive liver disease which culminates in liver failure or cancer. There is no vaccine and prevention programs have limited effectiveness, but new antiviral therapies now offer high rates of cure. This Program will evaluate strategies to improve the health of those affected and prevent new infections by better understanding of the virus and the body’s immune response, including scarring and liver cancer formation.
Toll Like Receptor signalling as a mediator of sex differences in pain, opioid and alcohol action. Brain immunology will be examined in this project to see if the signalling of a receptor called Toll Like Receptor 4 can explain sex differences in pain, and the action of pain killers and alcohol. These findings will have significant implications on the understanding of male and female brains, and will assist in the design of new drugs to treat brain and spinal cord diseases.