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
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
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
Tapasin And Major Histocompatibility Complex Class I Antigen Presentation
Funder
National Health and Medical Research Council
Funding Amount
$226,650.00
Summary
An effective T cell response (cellular immune response) to infections is vital to a functional immune system. Normally, proteins are cleaved into small molecules called peptides and these peptides are in turn presented by Major Histocompatibility Complex molecules to T cells. However, we have only partial understanding of what determines the choice of peptides that are finally presented to T cells. Recent research suggests that a molecule called tapasin may also influence the choice of peptides. ....An effective T cell response (cellular immune response) to infections is vital to a functional immune system. Normally, proteins are cleaved into small molecules called peptides and these peptides are in turn presented by Major Histocompatibility Complex molecules to T cells. However, we have only partial understanding of what determines the choice of peptides that are finally presented to T cells. Recent research suggests that a molecule called tapasin may also influence the choice of peptides. This research proposal aims to examine the role of tapasin in this regard. A thorough understanding of the basic principles of peptide presentation to T cells is crucial to the design of effective vaccines. Furthermore it will also broaden our understanding of immunological responses to cancer, autoimmune diseases and infections.Read moreRead less
ARC Centre of Excellence in Plants for Space. ARC Centre of Excellence in Plants for Space. This Centre aims to create on-demand, zero-waste, high-efficiency plants and plant products to address grand challenges in sustainability for Space and on Earth. Significant advances in plant, food, and sensory science; process and systems engineering; law and policy; and psychology are expected to deliver transformative solutions for Space habitation – and create enhanced plant-derived food and bioresour ....ARC Centre of Excellence in Plants for Space. ARC Centre of Excellence in Plants for Space. This Centre aims to create on-demand, zero-waste, high-efficiency plants and plant products to address grand challenges in sustainability for Space and on Earth. Significant advances in plant, food, and sensory science; process and systems engineering; law and policy; and psychology are expected to deliver transformative solutions for Space habitation – and create enhanced plant-derived food and bioresources to capitalise upon emergent and rapidly expanding domestic and global markets. Anticipated outcomes include industry uptake of innovative plant forms, foods, technologies, and commodities; and an ambitious education and international co-ordination agenda to position Australia as a global leader in research supporting Space habitation.Read moreRead less