Robotics and Machine Learning

Supervisor: Dr R Gross

Project Description

This project explores a novel artificial intelligence method, called Turing Learning [2], that enables robots to model themselves, each other or their environment without prior information. Turing Learning generalizes GANs - one of the most promising machine learning methods [3], making it applicable to real-time modelling [4].

Turing Learning refers to a family of machine learning algorithms that generate models and discriminators in a competitive setting [2]. In Turing Learning, a training agent, T, provides genuine data samples (the training data), while a model agent, M, provides counterfeit data samples. A discriminator agent, D, labels the data samples as either genuine or counterfeit. Agents M and D are being optimized: D is rewarded for labelling data samples correctly, while M is rewarded for misleading D (to label its data samples as genuine). The underlying idea of Turing Learning is inspired by the Turing test.

The project will be carried out in the Natural Robotics Lab [1].

[1] http://naturalrobotics.group.shef.ac.uk
[2] Turing learning: a metric-free approach to inferring behavior and its application to swarms. http://dx.doi.org/%2010.1007/s11721-016-0126-1
[3] GANs https://en.wikipedia.org/wiki/Generative_adversarial_network
[4] Generalizing GANs: A Turing Perspective https://nips.cc/Conferences/2017/Schedule?showEvent=9402

Prospective applicants should have a degree in a relevant subject (computer science, engineering, machine learning, artificial intelligence, mathematics, physics, robotics etc) and good experience in programming.

Contact Details

Dr R Gross

  • Tel: +44 (0) 114 222 5610

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