Human models for accelerated robot learning and human-robot interaction. This project aims to develop novel approaches to teach robots to proficiently interact with humans in a safe and low-cost manner. To achieve this aim, this project will develop novel models from which various human behaviours can be generated and used to train human-robot interaction policies in simulation. Expected outcomes of this project include new computational models of human behaviour built using cognitive science th ....Human models for accelerated robot learning and human-robot interaction. This project aims to develop novel approaches to teach robots to proficiently interact with humans in a safe and low-cost manner. To achieve this aim, this project will develop novel models from which various human behaviours can be generated and used to train human-robot interaction policies in simulation. Expected outcomes of this project include new computational models of human behaviour built using cognitive science theories and limited data and new training schemes for robot learning in simulation. By training robots in simulation with accurate human models, this research will enable fast and safe robot training to support the deployment and adoption of robots in human contexts such as healthcare facilities, homes, and workplaces.Read moreRead less
Self-supervised feature learning for rapid processing of marine imagery. Fast and reliable quantitative estimates of marine environmental health are needed for scientific studies, design and management of protected areas, and regulatory compliance of industrial activity in the ocean. Australia is collecting seafloor images at increasing rates but expert annotations are not keeping up, meaning that typical machine learning approaches struggle. This project will develop self-supervised techniques ....Self-supervised feature learning for rapid processing of marine imagery. Fast and reliable quantitative estimates of marine environmental health are needed for scientific studies, design and management of protected areas, and regulatory compliance of industrial activity in the ocean. Australia is collecting seafloor images at increasing rates but expert annotations are not keeping up, meaning that typical machine learning approaches struggle. This project will develop self-supervised techniques that use large amounts of unlabeled data to enhance performance. Our design takes advantage of additional information available for marine imagery such as geolocation and remote sensing context. We will explore how these representations can guide additional sampling and improve performance in classification tasks.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100960
Funder
Australian Research Council
Funding Amount
$420,198.00
Summary
Reverse Design of Tuneable 4D Printed Materials for Soft Robotics. This project aims to facilitate the design and manufacture of specialised objects that can change their shape over time. These types of objects are made from ‘tuneable metamaterials’, which can be made by 4D printing: 3D printing with an added dimension of time. These materials are becoming indispensable in many fields- including non-metallic soft robots used in medicine or the exploration of harsh environments like space- but ar ....Reverse Design of Tuneable 4D Printed Materials for Soft Robotics. This project aims to facilitate the design and manufacture of specialised objects that can change their shape over time. These types of objects are made from ‘tuneable metamaterials’, which can be made by 4D printing: 3D printing with an added dimension of time. These materials are becoming indispensable in many fields- including non-metallic soft robots used in medicine or the exploration of harsh environments like space- but are currently onerous to make. This project will develop a revolutionary new method for a user to work backward from defining the desired qualities to the manufacture of the object that satisfies their needs. It will also create a library that will allow users to quickly select a material that will be appropriate.Read moreRead less
Home helper robots: Understanding our future lives with human-like AI. This fellowship aims to understand and plan for the social effects of embedding ‘cute’ home helper robots into people’s everyday lives. The project is expected to generate new knowledge and resources to understand and respond to the emerging opportunities and risks associated with home helper robots, including their ability to support household tasks, and to provide child and aged care and companionship. Expected outcomes inc ....Home helper robots: Understanding our future lives with human-like AI. This fellowship aims to understand and plan for the social effects of embedding ‘cute’ home helper robots into people’s everyday lives. The project is expected to generate new knowledge and resources to understand and respond to the emerging opportunities and risks associated with home helper robots, including their ability to support household tasks, and to provide child and aged care and companionship. Expected outcomes include an improved understanding of anthropomorphised robots in everyday life and innovation in home helper robot theory and imaginaries. This should provide benefits such as informing robot design and policy to improve social outcomes, consumer protections and human-robot relationships.Read moreRead less