Visual learning and understanding

The Visual Learning and Understanding research program investigates the fundamental problem of how a robot can learn to reliably interpret its environment, and build an internal representation of its surroundings in order to decide on its actions.

The program addresses research questions such as how machine learning for visual perception can be made safe, trustworthy, and reliable; how robots can understand and represent the geometry, semantics, and functionality of their surroundings and the task-relevant objects therein; and how robots can use this internal representation and learn to decide or plan their next actions in order to accomplish a useful task in a safe way.

Projects

PhD Topics

Team

Led by Associate Professor Niko Sünderhauf

• Ahmed Abbas
  

  Research Assistant

• Cameron Coombe
  

  PhD Researcher

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• Chamuditha Jauanga
  

  PhD Researcher

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• Dr Dimity Miller
  

  Lecturer (Reliable robotic vision, Deep learning, Computer vision, and Robotics, SLAM)

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• Dr Faris Azhari
  

  Alumni

• Dr Feras Dayoub
  

  Adjunct Senior Lecturer

• Frank Loewenich
  

  MPhil Researcher

• Dr Frederic Maire
  

  Senior Lecturer (Waste, Soft Robotics, Environmental Robotics)

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• Kieren Astin-Walmsley
  

  MPhil Researcher

• Dr Krishan Rana
  

  Research Fellow

• Melih Guenes Minareci
  

  Research Fellow

• Nicolas Guillermo Marticorena Vidal
  

  PhD Researcher

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• Prof Niko Suenderhauf
  

  Deputy Centre Director, Program Lead Visual Learning and Understanding (Robotic Vision, Simultaneous Localisation and Mapping (SLAM), Reinforcement Learning, Machine Learning, AI)

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• Dr Quazi Marufur Rahman
  

  Alumni

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• Serena Mou
  

  Research Engineer

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• Sophie Taylor
  

  PhD Researcher

• Tjeard Douwe van Oort
  

  PhD Researcher