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Using machine learning to give robots new kinds of visual sensing


This work will develop the machine learning techniques and principles needed for robots to automatically interpret and benefit from a broad range of emerging imaging technologies.


Dr Donald Dansereau.

Research location

Aerospace, Mechanical and Mechatronic Engineering

Program type



Recent imaging advances have yielded super-human perceptual capabilities like imaging around corners, recording a person's pulse from changes in skin colour during a heartbeat, and directly imaging a pulse of light as it propagates through a scene. These technologies do not see the world the same way a conventional camera does, and making use of them in robotics raises important new challenges.

In this project you will expand current ideas in machine learning to bridge a gap between robotics and an expanding array of exciting new imaging technologies. Potential approaches include unsupervised and semi-supervised learning, active autonomous data collection, online learning, and new neural processing elements and architectures. Imaging technologies might include solid-state LiDAR, single-photon sensors, transient/femtosecond imaging, light field imaging, imaging around corners, and event-based dynamic vision sensors. Applications arise anywhere robots encounter perceptual challenges including all-weather autonomous driving, drone flight, underwater survey, human-robot interaction, and locomotion on challenging terrain.

Additional information

Working within the Australian Centre for Field Robotics (ACFR), you will have access to the state-of-the-art robots, facilities, dedicated technical staff, and mentorship available through this world-class research centre. The ACFR undertakes significant field robotics programs in autonomous driving, flight, agriculture, and underwater survey, providing rich opportunities for deployment and validation of novel perception systems.

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Opportunity ID

The opportunity ID for this research opportunity is 2631

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