Feature reinforcement learning. Agent applications include speech recognition systems, vision systems, search engines, auto-pilots, spam filters, and robots. The research outputs from this project will enable agents to adapt to their environment and automatically, during deployment, acquire much of the knowledge that is currently required to be built in by agent designers.
On-line planning for constrained autonomous agents in an uncertain world. This project aims to develop admissible heuristics for constrained stochastic planning problems and integrate them into state-of-the-art on-line algorithms. Artificial Intelligence (AI) systems, such as self-driving cars or energy management systems, make intelligent decisions to act near-optimally in uncertain environments, leading to savings, for example in energy use. But we also want assurances that AI systems will obe ....On-line planning for constrained autonomous agents in an uncertain world. This project aims to develop admissible heuristics for constrained stochastic planning problems and integrate them into state-of-the-art on-line algorithms. Artificial Intelligence (AI) systems, such as self-driving cars or energy management systems, make intelligent decisions to act near-optimally in uncertain environments, leading to savings, for example in energy use. But we also want assurances that AI systems will obey safety constraints. Solving constrained stochastic planning problems is key to building AI that is both robust and safe. This project will explore ways to account for uncertainty and constraints in heuristic estimation.Read moreRead less
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.
Decision making for lifetime affordable and tenable city housing. This project will study home buying decisions and outcomes and use this to provide new insights into housing affordability and liveability. The project will develop an innovative software tool for Australia's home buyers to explore affordability and liveability during home buying, and agent-based modelling of scenarios for urban development futures.
Navigating brains: the neurobiology of spatial cognition. Navigation is one of the most crucial and most challenging problems animals face. Behavioural analyses have shown that animals make use of a number of different mechanisms to navigate, but very little is known of how different forms of spatial information are processed and integrated by the brain. The project aims to tackle this by placing tethered ants in a virtual-reality simulation of their real environment allowing precise control of ....Navigating brains: the neurobiology of spatial cognition. Navigation is one of the most crucial and most challenging problems animals face. Behavioural analyses have shown that animals make use of a number of different mechanisms to navigate, but very little is known of how different forms of spatial information are processed and integrated by the brain. The project aims to tackle this by placing tethered ants in a virtual-reality simulation of their real environment allowing precise control of visual navigational cues, as well as the opportunity to study the brains of the tethered ants as they solve the real-world challenge of finding home. This may reveal how simple brains efficiently solve navigational tasks, which may inform both cognitive biology and bio-inspired computation.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
A high-speed light-weight embedded vision system for robotics and computer vision applications. Eyesight is the strongest sense for humans and much of the brain is dedicated to vision processing. This project aims to develop an analogous vision capability for small, dynamic robots: a small, light, camera that preprocesses the raw video data to provide higher level information to the robot.
Acquiring and Using Views for Homing. The aim of the project is to investigate how insects acquire and use scene memories for homing, a crucially important task for most animals. In bees and wasps, these memories are acquired during learning flights when leaving the nest. This fast, active learning process underpins much of the insects' navigational competence, but it remains unknown how it is controlled and how in detail memories guide returns to the nest. It is intended to use the latest camer ....Acquiring and Using Views for Homing. The aim of the project is to investigate how insects acquire and use scene memories for homing, a crucially important task for most animals. In bees and wasps, these memories are acquired during learning flights when leaving the nest. This fast, active learning process underpins much of the insects' navigational competence, but it remains unknown how it is controlled and how in detail memories guide returns to the nest. It is intended to use the latest camera-based reconstruction tools for the first time: to quantify the navigational information content of habitats including the visual information available to learning and homing insects, and to dynamically modify the insects’ natural visual environment in order to critically test hypotheses about acquisition and use of views for homing.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
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE130100203
Funder
Australian Research Council
Funding Amount
$385,000.00
Summary
Autonomous benthic observing system. This project seeks to improve our ability to monitor marine habitats and characterise their variability by enhancing the Integrated Marine Observing system (IMOS) Autonomous Underwater Vehicle (AUV) Facility. The new AUV infrastructure will reduce operating costs, increase robustness of the sampling effort and insure continued operation for the next decade.