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.
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
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
The role of strong duality in computer vision. This project aims to undertake research in the fields of computer vision and optimization that will have a significant impact on the design of numerical algorithms for solving a wide range of problems in Computer Vision, Virtual Reality and Robotic Navigation. This project will advance understanding of a broad class of problems related to how computers interpret images. An expected outcome is the generation of novel mathematical theory and numerical ....The role of strong duality in computer vision. This project aims to undertake research in the fields of computer vision and optimization that will have a significant impact on the design of numerical algorithms for solving a wide range of problems in Computer Vision, Virtual Reality and Robotic Navigation. This project will advance understanding of a broad class of problems related to how computers interpret images. An expected outcome is the generation of novel mathematical theory and numerical algorithms capable of fundamentally changing the way problems relevant to a wide range of vision-related applications are solved. This should offer Australia a strong competitive advantage as a leader in scientific innovation in the areas of Computer Vision, Virtual Reality and Robotics and Autonomous Systems.Read moreRead less
Human Cues for Robot Navigation. The world has many navigational cues for the benefit of humans: sign posts, maps and the wealth of information on the internet. Yet, to date, robotic navigation has made little use of this abundant symbolic information as a resource. This project will develop a robot navigation system that can navigate using information beyond the robot's range sensors by incorporating knowledge gained by reading room labels, following human route directions or interpreting maps ....Human Cues for Robot Navigation. The world has many navigational cues for the benefit of humans: sign posts, maps and the wealth of information on the internet. Yet, to date, robotic navigation has made little use of this abundant symbolic information as a resource. This project will develop a robot navigation system that can navigate using information beyond the robot's range sensors by incorporating knowledge gained by reading room labels, following human route directions or interpreting maps found on the web. This project will demonstrate the robot's navigation ability by comparing its performance with a human as it learns to find its way around campus by asking for directions, reading signs and maps, and searching the internet for clues.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
Superhuman place recognition with a unified model of human visual processing and rodent spatial memory. Current robotic and personal navigation systems leave much to be desired; GPS only works in open outdoor areas, lasers are expensive and cameras are highly sensitive to changing environmental conditions. In contrast, nature has evolved superb navigation systems. This project aims to solve the challenging problem of place recognition, a key component of navigation, by modelling the visual recog ....Superhuman place recognition with a unified model of human visual processing and rodent spatial memory. Current robotic and personal navigation systems leave much to be desired; GPS only works in open outdoor areas, lasers are expensive and cameras are highly sensitive to changing environmental conditions. In contrast, nature has evolved superb navigation systems. This project aims to solve the challenging problem of place recognition, a key component of navigation, by modelling the visual recognition skills of humans and the rodent spatial memory system. This project looks to combine the best understood and most capable components of place recognition in nature to create a whole more capable than its parts, produce advances in robotic and personal navigation technology and lead to breakthroughs in understandings of the brain.Read moreRead less