Implement reinforcement learning for robots in hardware to handle the reality gap

In the Autonomous Robotic Evolution project robots for different types of challenging environments are autonomously designed and fabricated with the Robot Fabricator. In the current implementation episode learning is used in hardware, however, this method has proven to be slow in real time. This research programme will consider different learning methods, for robots in hardware to enhance their controllers. The project will use the resulting hardware to improve the results in the robot simulation.

How to apply:

Applicants should apply via the University’s online application system . Please read the application guidance first so that you understand the various steps in the application process.

Funding

This is a self-funded project and you will need to have sufficient funds in place (eg from scholarships, personal funds and/or other sources) to cover the tuition fees and living expenses for the duration of the research degree programme. Please check the School of Physics, Engineering and Technology website for details about funding opportunities at York.