Artificial Intelligence and Robotics Group (AIR)

The patent is based on the research presented in the paper Biologically Inspired Rate Control of Chaos. It exploits the ability to control complex physical systems using the nonlinear control method to control combustion engines. The patent also shows the ability to control other non-linear and chaotically perturbed systems, such as wind-turbines, and bioreactors. The proof of concept engine has shown the validity of the approach and the patent covers the innovative method that can allow energy efficiency, maintain desired low emission power domains, and even allow fuel neutral engines. 

Olde Scheper, T. V. S. M., & Carnell, A. R. (2013). A method of controlling a dynamic physical system that exhibits a chaotic behaviour. Patent (Awarded 2018). Retrieved from https://patents.google.com/patent/WO2013064840A1

A project with the law firm Moorcrofts LLP is funded by InnovateUK with £284.000. The group contributed machine learning and natural language processing solutions for contract law analysis. The commercial outcome is ContrAI, a smart contract management suit in which AI helps to identify important clauses in contracts to make their processing better and more cost-effective. 

A main research focus of the group is efficient motor learning with Goal Babbling, which was initially described by Rolf, Steil, and Gienger in 2010. Recent experiments include a research student project on learning aimed-throwing skills (MSc by Research, Jamie Pierce, 2020), and a collaboration with the Humanoid and Cognitive Robotics group at CTU Prague investigates the learning of tactile body models by means of goal babbling (Gama, Shcherban, Rolf, Hoffman, 2020 & 2021).

A key research area of the group, and frequent application context of the machine learning methods is autonomous decision making in moral contexts. An ongoing PhD project by Rebecca Raper investigates learning architectures for autonomous moral agents. A particular focus is the formation of social norms (Matthew Wilson), as well as particular social norms in the context of robotic applications such as proxemics (Vaswani Bhavnani & Rolf, 2020). 

This method has already been applied within the Criticality Analysis of Diabetic Gait in Children (Cardigan) project in collaboration with the children’s Hospital Infantil Federico Gomez, Mexico, to allow gait of children to be used as markers for their clinical progression during their treatment for obesity. Led by Dr Arantza Aldea and funded by the British Council 2019.

This project was funded by the Academy of Medical Sciences and was a partnership between OBU and Universidad Antonio Nariño, Colombia. The aim of the project was to explore the feasibility of a cloud-based platform for diabetes management and research. It focused on the specific needs in the Colombian population and considered the potential for embedding AI within an open source cloud-based platform. The work has led to the development of a bespoke Spanish language platform (CLOUDI) that is currently been evaluated in a large-scale clinical study.