Scene understanding and reliable deep learning for robotics

Study level

Vacation research experience scheme

Topic status

We're looking for students to study this topic.

Supervisors

Dr Niko Suenderhauf
Position
Senior Lecturer
Division / Faculty
Science and Engineering Faculty

Overview

For many practical applications of robotics and embodied AI, such as driverless cars, healthcare and domestic service robotics, scene understanding is a critical component. Modern methods rely heavily on deep learning to extract information about the scene from camera images.

Research activities

You will support our ongoing research in the areas of:

  • semantic scene understanding and semantic SLAM
  • probabilistic object detection
  • reliability of deep learning for robotics applications
  • reinforcement learning based on semantic information.
Based on your interests, we can tailor the project to be in a certain area of research. Your role may be software-heavy or involve working with real robots.

Outcomes

Depending on the chosen project topic potential outcomes may include a software module that demonstrates a certain capability on a robot or a dataset, or a component that supports the research of one of our PhD students and can be used in a publication (evaluation, experiments, etc.).

Skills and experience

Depending on your chosen topic the requires skills vary.

In general you should be good at programming in Python and have a good understanding of linear algebra.

Scholarships

You may be able to apply for a research scholarship in our annual scholarship round.

Annual scholarship round

Keywords

Contact

Contact the supervisor for more information.