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Active Visual Navigation in an Unexplored Environment

Primary supervisor

Hamid Rezatofighi

In this project, the goal is to develop a new method (using computer vision and machine learning techniques) for robotic navigation in which goals can be specified at a much higher level of abstraction than has previously been possible. This will be achieved using deep learning to make informed predictions about a scene layout and navigating as an active observer in which the predictions inform actions. The outcome will be robotic agents capable of effective and efficient navigation and operation in previously unseen environments, and the ability to control such agents with more human-like instructions. Such capabilities are desirable, and in some cases essential, for autonomous robots in a variety of important application areas including automated warehousing and high-level control of autonomous vehicles.


Student cohort

Double Semester



Required knowledge

  1. Good coding skills in a variety of coding languages
  2. Previous experience working with deep learning models for different tasks
  3. ​​​​​Proficient programming skills in Python and one of the main deep learning libraries (e.g., TensorFlow, PyTorch, Keras)