Ian Manchester

Ian is the Hub Director and will oversee the full scope of research and demonstrator programs along with Deputy Directors Niko Suenderhauf and Stefan Williams. Within the Hub, his personal research activities will mainly be in achieving robust autonomy in challenging conditions, for example, marine robots in strong currents or UAVs in high winds; incorporating high-dimensional data streams (such as video and laser scans) into real-time perception and control algorithms; learning dexterous mobile manipulation from demonstration and interaction; and scalable algorithms for planning under uncertainty.

Bio: Ian Manchester is a Professor of Mechatronic Engineering at the University of Sydney, Australia, and specialises in the field of control engineering and robotics. His fundamental research work has been motivated by problems in the control of walking robots involving transformative technology which has many applications in both the engineered and natural world. These include transportation systems, chemical processes, aerospace, biological systems and intelligent robotic systems.

After receiving his PhD degree in electrical engineering from the University of New South Wales, Ian worked as a post-doctoral researcher at Umea University, Sweden, and then as a Research scientist at the Massachusetts Institute of Technology (MIT). In 2019, Ian was identified as a future research leader under the University of Sydney Research Accelerator (SOAR) Fellowship scheme. Currently, he is also the Director of the Australian Centre for Field Robotics (ACFR), one of the largest robotics research organisations in the world, internationally renowned for its fundamental research in robot perception and control and the development of autonomous systems operating in complex outdoor environments.

In the past five years, Ian has been invited to give seminars at top universities around the world including: Harvard, Caltech, MIT, University of California, Lund University and KTH Stockholm.

Research Expertise: Ian’s main research is in algorithms for control, estimation, and learning of complex dynamical systems in robotics and other application domains. A major current research focus is novel machine-learning algorithms with guarantees of safety and stability, and applications in safety-critical robotic systems.

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